SharkTank India (Season 1,2,3) Exploratory Data Analysis (EDA) 🦈
¶from IPython.display import IFrame
import datetime
print("Notebook was last executed on:", datetime.date.today().strftime("%Y-%b-%d"), "with Python version")
!python --version
Notebook was last executed on: 2024-Mar-25 with Python version Python 3.11.5
# Source: Wikipedia
IFrame('https://upload.wikimedia.org/wikipedia/en/2/2f/Shark_Tank_India.jpg', width=330, height=330)
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import matplotlib.pyplot as plt
import seaborn as sns
from babel.numbers import format_currency
from wordcloud import WordCloud, STOPWORDS
import geopandas as gpd
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_dark"
pio.renderers.default = 'notebook'
shark_tank = pd.read_csv('Shark Tank India.csv', encoding = "ISO-8859-1")
nRow, nCol = shark_tank.shape
print(f'\nThere are {nRow} rows and {nCol} columns in the dataset')
total 124 -rw-r--r-- 1 nobody nogroup 124427 Mar 16 07:41 'Shark Tank India.csv' There are 441 rows and 78 columns in the dataset
shark_tank = pd.read_csv('Shark Tank India.csv',encoding = "ISO-8859-1")
nRow, nCol = shark_tank.shape
print(f'\nThere are {nRow} rows and {nCol} columns in the dataset')
There are 320 rows and 64 columns in the dataset
# Word cloud based on episode titles
text = " Shark Tank India ".join(cat for cat in shark_tank.loc[shark_tank['Episode Title'].notnull()]['Episode Title'])
stop_words = list(STOPWORDS) + ["Ka", "Ki", "Ke", "Ko", "Se", "Hai", "Ek"]
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='white', colormap='Reds', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(25,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
shark_tank.head(8)
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | BluePineFoods | 1 | 1 | 20-Dec-21 | 4-Feb-22 | 20-Dec-21 | Badlegi Business Ki Tasveer | Rannvijay Singh | Food | Frozen Momos | https://bluepinefoods.com/ | 2016.0 | 3 | 2.0 | 1.0 | NaN | 0.0 | Middle | Delhi | Delhi | 95.0 | 8.0 | NaN | NaN | ... | 25.0 | 5.33 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 25.0 | 5.33 | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
| 1 | 1 | BoozScooters | 1 | 2 | 20-Dec-21 | 4-Feb-22 | 20-Dec-21 | Badlegi Business Ki Tasveer | Rannvijay Singh | Vehicles/Electrical Vehicles | Renting e-bike for mobility in private spaces | https://www.boozup.net/ | 2017.0 | 1 | 1.0 | NaN | NaN | 0.0 | Young | Ahmedabad | Gujarat | 4.0 | 0.4 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 20.0 | 25.00 | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
| 2 | 1 | HeartUpMySleeves | 1 | 3 | 20-Dec-21 | 4-Feb-22 | 20-Dec-21 | Badlegi Business Ki Tasveer | Rannvijay Singh | Beauty/Fashion | Detachable Sleeves | https://heartupmysleeves.com/ | 2021.0 | 1 | NaN | 1.0 | NaN | 0.0 | Young | Delhi | Delhi | NaN | 2.0 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
| 3 | 1 | TagzFoods | 2 | 4 | 20-Dec-21 | 4-Feb-22 | 21-Dec-21 | Insaan, Ideas Aur Sapne | Rannvijay Singh | Food | Healthy Potato Chips Snacks | https://tagzfoods.com/ | 2019.0 | 2 | 2.0 | NaN | NaN | 0.0 | Middle | Bangalore | Karnataka | 700.0 | NaN | 48.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 70.0 | 2.75 | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
| 4 | 1 | HeadAndHeart | 2 | 5 | 20-Dec-21 | 4-Feb-22 | 21-Dec-21 | Insaan, Ideas Aur Sapne | Rannvijay Singh | Education | Brain Development Course | https://thehnh.in/ | 2015.0 | 4 | 1.0 | 3.0 | NaN | 1.0 | Middle | Patiala | Punjab | 30.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
| 5 | 1 | Agritourism | 2 | 6 | 20-Dec-21 | 4-Feb-22 | 21-Dec-21 | Insaan, Ideas Aur Sapne | Rannvijay Singh | Agriculture | Tourism | https://www.agritourism.in/ | 2005.0 | 2 | 1.0 | 1.0 | NaN | 1.0 | Middle | Baramati | Maharashtra | 79.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
| 6 | 1 | qZenseLabs | 3 | 7 | 20-Dec-21 | 4-Feb-22 | 22-Dec-21 | Aam Aadmi Ke Business Ideas | Rannvijay Singh | Food | Food Freshness Detector | https://www.qzense.com/ | 2020.0 | 2 | NaN | 2.0 | NaN | 0.0 | Middle | Delhi,Mohali | Delhi,Punjab | 25.0 | 15.0 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
| 7 | 1 | Peeschute | 3 | 8 | 20-Dec-21 | 4-Feb-22 | 22-Dec-21 | Aam Aadmi Ke Business Ideas | Rannvijay Singh | Beauty/Fashion | Disposable Urine Bag | https://www.peeschute.com/ | 2019.0 | 1 | 1.0 | NaN | NaN | 0.0 | Young | Jalna | Maharashtra | 100.0 | NaN | NaN | NaN | ... | 75.0 | 6.00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
8 rows × 78 columns
shark_tank.tail(10).T
| 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Season Number | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| Startup Name | Myracle.io | Cup-ji | AToddlerThing | FlexifyMe | Dharaksha | iDreamCareer | RockPaperRum | Fit&Flex | Sukham | Smotect |
| Episode Number | 37 | 38 | 38 | 38 | 39 | 39 | 39 | 40 | 40 | 40 |
| Pitch Number | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 |
| Season Start | 22-Jan-24 | 22-Jan-24 | 22-Jan-24 | 22-Jan-24 | 22-Jan-24 | 22-Jan-24 | 22-Jan-24 | 22-Jan-24 | 22-Jan-24 | 22-Jan-24 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| Aman Present | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| Peyush Present | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| Amit Present | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN |
| Ashneer Present | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Guest Present | 1.0 | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
78 rows × 10 columns
shark_tank['Season Number'] = shark_tank['Season Number'].astype(pd.Int32Dtype())
shark_tank['Episode Number'] = shark_tank['Episode Number'].astype(pd.Int32Dtype())
shark_tank['Pitch Number'] = shark_tank['Pitch Number'].astype(pd.Int32Dtype())
shark_tank['Number of Presenters'] = shark_tank['Number of Presenters'].astype(pd.Int32Dtype())
shark_tank['Male Presenters'] = shark_tank['Male Presenters'].astype(pd.Int32Dtype())
shark_tank['Female Presenters'] = shark_tank['Female Presenters'].astype(pd.Int32Dtype())
shark_tank['Transgender Presenters'] = shark_tank['Transgender Presenters'].astype(pd.Int32Dtype())
shark_tank['Couple Presenters'] = shark_tank['Couple Presenters'].astype(pd.Int32Dtype())
shark_tank['Gross Margin'] = shark_tank['Gross Margin'].astype(pd.Int32Dtype())
shark_tank['Net Margin'] = shark_tank['Net Margin'].astype(pd.Int32Dtype())
shark_tank['Started in'] = shark_tank['Started in'].astype(pd.Int32Dtype())
shark_tank['Yearly Revenue'] = shark_tank['Yearly Revenue'].astype(pd.Int32Dtype())
shark_tank['Received Offer'] = shark_tank['Received Offer'].astype(pd.Int32Dtype())
shark_tank['Accepted Offer'] = shark_tank['Accepted Offer'].astype(pd.Int32Dtype())
shark_tank.sample(10).style.set_properties(**{"background-color": "#2a9d8f","color":"white","border": "1px solid black", 'font-size': '10pt'})
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | EBITDA | Cash Burn | SKUs | Has Patents | Bootstrapped | Original Ask Amount | Original Offered Equity | Valuation Requested | Received Offer | Accepted Offer | Total Deal Amount | Total Deal Equity | Total Deal Debt | Debt Interest | Deal Valuation | Number of Sharks in Deal | Deal Has Conditions | Royalty Deal | Advisory Shares Equity | Namita Investment Amount | Namita Investment Equity | Namita Debt Amount | Vineeta Investment Amount | Vineeta Investment Equity | Vineeta Debt Amount | Anupam Investment Amount | Anupam Investment Equity | Anupam Debt Amount | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | 1 | Gizmoswala | 0 | 137 | 20-Dec-21 | 4-Feb-22 | nan | Unseen | Rannvijay Singh | Entertainment | Sex toys and games | https://www.gizmoswala.com/ | 2020 | 3 | 2 | 1 | 0 | Middle | Mumbai | Maharashtra | 7.000000 | 40 | nan | nan | nan | nan | nan | 75.000000 | 5.000000 | 1500.000000 | 0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | ||||
| 227 | 2 | SharmaJiKiAata | 25 | 228 | 2-Jan-23 | 10-Mar-23 | 3-Feb-23 | Badhta India | Rahul Dua | Food | Freshly milled atta | https://sharmajikaaata.com/ | 2019 | 2 | 1 | 1 | 0 | Middle | Pune | Maharashtra | nan | 63 | 38 | nan | nan | nan | nan | nan | 40.000000 | 10.000000 | 400.000000 | 1 | 1 | 40.000000 | 20.000000 | nan | nan | 200.000000 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 40.000000 | 20.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 1.000000 | 1.000000 | 1.000000 | 1.000000 | nan | 1.000000 | nan | nan | ||
| 299 | 2 | ZSportsTech | 46 | 300 | 2-Jan-23 | 10-Mar-23 | 6-Mar-23 | Different Colours Of Entrepreneurship | Rahul Dua | Sports | Cricket Sport Shop | https://www.zsportstech.com/ | 2 | 2 | 0 | Middle | Mumbai | Maharashtra | 31 | nan | nan | nan | nan | nan | nan | 60.000000 | 2.000000 | 3000.000000 | 0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | nan | nan | nan | ||||||
| 7 | 1 | Peeschute | 3 | 8 | 20-Dec-21 | 4-Feb-22 | 22-Dec-21 | Aam Aadmi Ke Business Ideas | Rannvijay Singh | Beauty/Fashion | Disposable Urine Bag | https://www.peeschute.com/ | 2019 | 1 | 1 | 0 | Young | Jalna | Maharashtra | 100 | nan | nan | nan | 2.000000 | nan | nan | 75.000000 | 4.000000 | 1875.000000 | 1 | 1 | 75.000000 | 6.000000 | nan | nan | 1250.000000 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 75.000000 | 6.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 1.000000 | 1.000000 | 1.000000 | 1.000000 | nan | nan | 1.000000 | nan | ||||
| 428 | 3 | DesignTemplate | 36 | 429 | 22-Jan-24 | nan | 11-Mar-24 | Designing Dreams | Rahul Dua | Technology/Software | Online Design Marketplace | https://designtemplate.io/ | 1 | 1 | 0 | Middle | Bid | Maharashtra | 160 | 16.000000 | nan | nan | nan | nan | nan | 100.000000 | 2.500000 | 4000.000000 | 1 | 1 | 100.000000 | 10.000000 | nan | nan | 1000.000000 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 100.000000 | 10.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | Ritesh Aggarwal,Radhika Gupta | nan | 1.000000 | nan | 1.000000 | 1.000000 | nan | nan | 2.000000 | |||||
| 288 | 2 | WTF | 43 | 289 | 2-Jan-23 | 10-Mar-23 | 1-Mar-23 | Creating Value Through Ideas | Rahul Dua | Food | Where's The Food | nan | 1 | 1 | 0 | Middle | Kolkata | West Bengal | nan | nan | nan | nan | nan | nan | 75.000000 | 5.000000 | 1500.000000 | 0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 1.000000 | nan | 1.000000 | 1.000000 | 1.000000 | 1.000000 | nan | nan | |||||||
| 171 | 2 | TheSimplySalad | 7 | 172 | 2-Jan-23 | 10-Mar-23 | 10-Jan-23 | Shaandar Businesses | Rahul Dua | Food | Freshly chopped salads | https://simplysalad.com/ | 2 | 1 | 1 | 0 | Young | Ahmedabad | Gujarat | nan | nan | nan | nan | nan | nan | 30.000000 | 10.000000 | 300.000000 | 1 | 1 | 30.000000 | 10.000000 | nan | nan | 300.000000 | 2.000000 | nan | nan | nan | nan | nan | nan | 15.000000 | 5.000000 | nan | nan | nan | nan | 15.000000 | 5.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | nan | nan | nan | |||||
| 425 | 3 | Namakwali | 35 | 426 | 22-Jan-24 | nan | 8-Mar-24 | Inspiring Women Entrepreneurs | Rahul Dua | Food | Organic Spices | https://www.namakwali.com/ | 2018 | 2 | 1 | 1 | 0 | Middle | nan | Uttarakhand | 38 | 3.000000 | 17 | nan | nan | nan | nan | nan | 50.000000 | 5.000000 | 1000.000000 | 1 | 1 | 10.000000 | 5.000000 | 40.000000 | 8.000000 | 200.000000 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 10.000000 | 5.000000 | 40.000000 | nan | nan | nan | nan | nan | nan | nan | nan | 1.000000 | 1.000000 | 1.000000 | 1.000000 | nan | 1.000000 | nan | nan | ||
| 132 | 1 | Glii | 0 | 133 | 20-Dec-21 | 4-Feb-22 | nan | Unseen | Rannvijay Singh | Services | Dating app for LGBTQ | https://www.glii.in/ | 2021 | 4 | 3 | 1 | 0 | Middle | Noida | Uttar Pradesh | nan | nan | nan | nan | nan | nan | 40.000000 | 4.000000 | 1000.000000 | 0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | |||||
| 386 | 3 | HouseOfBeautyIndia | 22 | 387 | 22-Jan-24 | nan | 20-Feb-24 | Impressive Numbers and High Stakes | Rahul Dua | Beauty/Fashion | Skin care Products | https://houseofbeautyindia.com/ | 2021 | 1 | 1 | 0 | Middle | Delhi | Delhi | 40.000000 | nan | nan | nan | nan | yes | 150.000000 | 5.000000 | 3000.000000 | 0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 1.000000 | 1.000000 | 1.000000 | 1.000000 | nan | 1.000000 | nan | nan |
shark_tank.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 441 entries, 0 to 440 Data columns (total 78 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Season Number 441 non-null Int32 1 Startup Name 441 non-null object 2 Episode Number 441 non-null Int32 3 Pitch Number 441 non-null Int32 4 Season Start 441 non-null object 5 Season End 321 non-null object 6 Original Air Date 410 non-null object 7 Episode Title 441 non-null object 8 Anchor 441 non-null object 9 Industry 441 non-null object 10 Business Description 441 non-null object 11 Company Website 430 non-null object 12 Started in 321 non-null Int32 13 Number of Presenters 441 non-null Int32 14 Male Presenters 381 non-null Int32 15 Female Presenters 206 non-null Int32 16 Transgender Presenters 3 non-null Int32 17 Couple Presenters 437 non-null Int32 18 Pitchers Average Age 441 non-null object 19 Pitchers City 437 non-null object 20 Pitchers State 438 non-null object 21 Yearly Revenue 210 non-null Int32 22 Monthly Sales 210 non-null float64 23 Gross Margin 117 non-null Int32 24 Net Margin 64 non-null Int32 25 EBITDA 16 non-null float64 26 Cash Burn 56 non-null object 27 SKUs 27 non-null float64 28 Has Patents 39 non-null object 29 Bootstrapped 30 non-null object 30 Original Ask Amount 441 non-null float64 31 Original Offered Equity 441 non-null float64 32 Valuation Requested 441 non-null float64 33 Received Offer 441 non-null Int32 34 Accepted Offer 301 non-null Int32 35 Total Deal Amount 248 non-null float64 36 Total Deal Equity 248 non-null float64 37 Total Deal Debt 53 non-null float64 38 Debt Interest 37 non-null float64 39 Deal Valuation 247 non-null float64 40 Number of Sharks in Deal 248 non-null float64 41 Deal Has Conditions 26 non-null object 42 Royalty Deal 14 non-null float64 43 Advisory Shares Equity 3 non-null float64 44 Namita Investment Amount 81 non-null float64 45 Namita Investment Equity 81 non-null float64 46 Namita Debt Amount 14 non-null float64 47 Vineeta Investment Amount 63 non-null float64 48 Vineeta Investment Equity 63 non-null float64 49 Vineeta Debt Amount 11 non-null float64 50 Anupam Investment Amount 70 non-null float64 51 Anupam Investment Equity 70 non-null float64 52 Anupam Debt Amount 7 non-null float64 53 Aman Investment Amount 101 non-null float64 54 Aman Investment Equity 101 non-null float64 55 Aman Debt Amount 14 non-null float64 56 Peyush Investment Amount 87 non-null float64 57 Peyush Investment Equity 87 non-null float64 58 Peyush Debt Amount 11 non-null float64 59 Amit Investment Amount 33 non-null float64 60 Amit Investment Equity 33 non-null float64 61 Amit Debt Amount 6 non-null float64 62 Ashneer Investment Amount 21 non-null float64 63 Ashneer Investment Equity 21 non-null float64 64 Ashneer Debt Amount 2 non-null float64 65 Guest Investment Amount 37 non-null float64 66 Guest Investment Equity 37 non-null float64 67 Guest Debt Amount 6 non-null float64 68 Invested Guest Name 37 non-null object 69 All Guest Names 116 non-null object 70 Namita Present 361 non-null float64 71 Vineeta Present 287 non-null float64 72 Anupam Present 390 non-null float64 73 Aman Present 383 non-null float64 74 Peyush Present 295 non-null float64 75 Amit Present 128 non-null float64 76 Ashneer Present 99 non-null float64 77 Guest Present 116 non-null float64 dtypes: Int32(14), float64(46), object(18) memory usage: 250.8+ KB
shark_tank.describe().T.round(2).style.background_gradient(cmap = 'Oranges')
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Season Number | 441.000000 | 1.927438 | 0.782882 | 1.000000 | 1.000000 | 2.000000 | 3.000000 | 3.000000 |
| Episode Number | 441.000000 | 21.215420 | 13.919291 | 0.000000 | 9.000000 | 21.000000 | 32.000000 | 51.000000 |
| Pitch Number | 441.000000 | 221.000000 | 127.449990 | 1.000000 | 111.000000 | 221.000000 | 331.000000 | 441.000000 |
| Started in | 321.000000 | 2018.791277 | 2.814591 | 1998.000000 | 2018.000000 | 2019.000000 | 2021.000000 | 2023.000000 |
| Number of Presenters | 441.000000 | 2.029478 | 0.829316 | 1.000000 | 1.000000 | 2.000000 | 2.000000 | 6.000000 |
| Male Presenters | 381.000000 | 1.695538 | 0.831426 | 1.000000 | 1.000000 | 2.000000 | 2.000000 | 6.000000 |
| Female Presenters | 206.000000 | 1.194175 | 0.420412 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 3.000000 |
| Transgender Presenters | 3.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Couple Presenters | 437.000000 | 0.180778 | 0.385275 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| Yearly Revenue | 210.000000 | 596.214286 | 1601.166969 | 0.000000 | 75.000000 | 170.000000 | 476.000000 | 18700.000000 |
| Monthly Sales | 210.000000 | 71.174200 | 259.176637 | 0.000000 | 5.625000 | 20.000000 | 59.500000 | 3500.000000 |
| Gross Margin | 117.000000 | 54.547009 | 21.149099 | 3.000000 | 40.000000 | 55.000000 | 69.000000 | 150.000000 |
| Net Margin | 64.000000 | 21.218750 | 12.547647 | 1.000000 | 10.750000 | 20.000000 | 30.000000 | 55.000000 |
| EBITDA | 16.000000 | 11.531250 | 12.898926 | -20.000000 | 5.000000 | 10.500000 | 18.250000 | 35.000000 |
| SKUs | 27.000000 | 342.185185 | 1157.611169 | 1.000000 | 9.000000 | 25.000000 | 110.000000 | 6000.000000 |
| Original Ask Amount | 441.000000 | 148.969872 | 1426.542757 | 0.000000 | 50.000000 | 70.000000 | 100.000000 | 30000.000000 |
| Original Offered Equity | 441.000000 | 3.789796 | 3.693267 | 0.200000 | 1.000000 | 2.500000 | 5.000000 | 30.000000 |
| Valuation Requested | 441.000000 | 5357.185214 | 9191.269411 | 0.000000 | 1000.000000 | 2600.000000 | 6000.000000 | 120000.000000 |
| Received Offer | 441.000000 | 0.682540 | 0.466017 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 |
| Accepted Offer | 301.000000 | 0.823920 | 0.381522 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Total Deal Amount | 248.000000 | 66.083119 | 43.601659 | 0.000000 | 40.000000 | 58.300000 | 90.000000 | 300.000000 |
| Total Deal Equity | 248.000000 | 8.531532 | 9.637837 | 0.500000 | 2.500000 | 5.000000 | 10.000000 | 75.000000 |
| Total Deal Debt | 53.000000 | 46.622642 | 27.182592 | 20.000000 | 25.000000 | 40.000000 | 50.000000 | 150.000000 |
| Debt Interest | 37.000000 | 10.432432 | 3.586553 | 0.000000 | 10.000000 | 10.000000 | 12.000000 | 18.000000 |
| Deal Valuation | 247.000000 | 2299.296411 | 3535.721814 | 0.000000 | 434.500000 | 1000.000000 | 2500.000000 | 25000.000000 |
| Number of Sharks in Deal | 248.000000 | 2.004032 | 1.143537 | 1.000000 | 1.000000 | 2.000000 | 3.000000 | 5.000000 |
| Royalty Deal | 14.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Advisory Shares Equity | 3.000000 | 1.533333 | 0.950438 | 0.600000 | 1.050000 | 1.500000 | 2.000000 | 2.500000 |
| Namita Investment Amount | 81.000000 | 32.911608 | 20.897607 | 0.000016 | 20.000000 | 28.300000 | 50.000000 | 100.000000 |
| Namita Investment Equity | 81.000000 | 4.005909 | 5.161855 | 0.200000 | 1.000000 | 2.080000 | 5.000000 | 25.000000 |
| Namita Debt Amount | 14.000000 | 41.082857 | 21.412774 | 12.500000 | 26.250000 | 40.500000 | 50.000000 | 100.000000 |
| Vineeta Investment Amount | 63.000000 | 31.504167 | 21.360443 | 0.002500 | 17.580000 | 25.000000 | 40.000000 | 100.000000 |
| Vineeta Investment Equity | 63.000000 | 4.292286 | 4.844934 | 0.200000 | 1.000000 | 2.500000 | 5.000000 | 25.000000 |
| Vineeta Debt Amount | 11.000000 | 24.923636 | 14.011572 | 12.500000 | 13.750000 | 20.000000 | 30.000000 | 50.000000 |
| Anupam Investment Amount | 70.000000 | 29.659325 | 21.892187 | 0.000000 | 17.500000 | 25.000000 | 40.000000 | 100.000000 |
| Anupam Investment Equity | 70.000000 | 4.816257 | 5.385230 | 0.166000 | 1.037500 | 2.500000 | 6.495000 | 25.000000 |
| Anupam Debt Amount | 7.000000 | 27.142857 | 16.100503 | 12.500000 | 16.250000 | 20.000000 | 37.500000 | 50.000000 |
| Aman Investment Amount | 101.000000 | 34.612599 | 24.824278 | 0.000000 | 17.500000 | 30.000000 | 50.000000 | 150.000000 |
| Aman Investment Equity | 101.000000 | 3.206758 | 4.518018 | 0.166000 | 1.000000 | 2.000000 | 4.000000 | 40.000000 |
| Aman Debt Amount | 14.000000 | 39.665714 | 18.308146 | 16.660000 | 26.250000 | 37.500000 | 47.915000 | 80.000000 |
| Peyush Investment Amount | 87.000000 | 34.602101 | 30.408193 | 0.000000 | 20.000000 | 28.000000 | 45.000000 | 250.000000 |
| Peyush Investment Equity | 87.000000 | 5.683115 | 10.959026 | 0.166000 | 1.000000 | 2.000000 | 5.000000 | 75.000000 |
| Peyush Debt Amount | 11.000000 | 31.090909 | 15.332675 | 10.000000 | 23.500000 | 25.000000 | 40.000000 | 60.000000 |
| Amit Investment Amount | 33.000000 | 36.193939 | 27.073697 | 3.500000 | 15.000000 | 25.000000 | 50.000000 | 100.000000 |
| Amit Investment Equity | 33.000000 | 4.497170 | 4.775754 | 0.330000 | 1.500000 | 3.000000 | 5.000000 | 20.000000 |
| Amit Debt Amount | 6.000000 | 37.500000 | 18.641352 | 10.000000 | 28.750000 | 40.000000 | 43.750000 | 65.000000 |
| Ashneer Investment Amount | 21.000000 | 25.682381 | 16.860620 | 1.000000 | 15.000000 | 20.000000 | 30.000000 | 70.000000 |
| Ashneer Investment Equity | 21.000000 | 4.440000 | 5.065662 | 1.000000 | 2.000000 | 3.000000 | 5.000000 | 25.000000 |
| Ashneer Debt Amount | 2.000000 | 57.000000 | 59.396970 | 15.000000 | 36.000000 | 57.000000 | 78.000000 | 99.000000 |
| Guest Investment Amount | 37.000000 | 38.423047 | 37.483512 | 0.000253 | 20.000000 | 30.000000 | 40.500000 | 200.000000 |
| Guest Investment Equity | 37.000000 | 3.343108 | 3.811857 | 0.200000 | 1.000000 | 2.330000 | 4.000000 | 17.500000 |
| Guest Debt Amount | 6.000000 | 32.553333 | 26.097530 | 12.500000 | 17.500000 | 25.000000 | 32.125000 | 83.320000 |
| Namita Present | 361.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Vineeta Present | 287.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Anupam Present | 390.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Aman Present | 383.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Peyush Present | 295.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Amit Present | 128.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Ashneer Present | 99.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Guest Present | 116.000000 | 1.155172 | 0.363640 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 |
# Unique values in each column
for col in shark_tank.columns:
print("Number of unique values in", col, "-", shark_tank[col].nunique())
Number of unique values in Season Number - 3 Number of unique values in Startup Name - 441 Number of unique values in Episode Number - 52 Number of unique values in Pitch Number - 441 Number of unique values in Season Start - 3 Number of unique values in Season End - 2 Number of unique values in Original Air Date - 125 Number of unique values in Episode Title - 126 Number of unique values in Anchor - 2 Number of unique values in Industry - 16 Number of unique values in Business Description - 438 Number of unique values in Company Website - 430 Number of unique values in Started in - 16 Number of unique values in Number of Presenters - 6 Number of unique values in Male Presenters - 6 Number of unique values in Female Presenters - 3 Number of unique values in Transgender Presenters - 1 Number of unique values in Couple Presenters - 2 Number of unique values in Pitchers Average Age - 3 Number of unique values in Pitchers City - 104 Number of unique values in Pitchers State - 42 Number of unique values in Yearly Revenue - 119 Number of unique values in Monthly Sales - 99 Number of unique values in Gross Margin - 42 Number of unique values in Net Margin - 30 Number of unique values in EBITDA - 14 Number of unique values in Cash Burn - 1 Number of unique values in SKUs - 23 Number of unique values in Has Patents - 2 Number of unique values in Bootstrapped - 2 Number of unique values in Original Ask Amount - 48 Number of unique values in Original Offered Equity - 31 Number of unique values in Valuation Requested - 97 Number of unique values in Received Offer - 2 Number of unique values in Accepted Offer - 2 Number of unique values in Total Deal Amount - 39 Number of unique values in Total Deal Equity - 49 Number of unique values in Total Deal Debt - 18 Number of unique values in Debt Interest - 8 Number of unique values in Deal Valuation - 93 Number of unique values in Number of Sharks in Deal - 5 Number of unique values in Deal Has Conditions - 1 Number of unique values in Royalty Deal - 1 Number of unique values in Advisory Shares Equity - 3 Number of unique values in Namita Investment Amount - 30 Number of unique values in Namita Investment Equity - 33 Number of unique values in Namita Debt Amount - 9 Number of unique values in Vineeta Investment Amount - 24 Number of unique values in Vineeta Investment Equity - 31 Number of unique values in Vineeta Debt Amount - 7 Number of unique values in Anupam Investment Amount - 29 Number of unique values in Anupam Investment Equity - 38 Number of unique values in Anupam Debt Amount - 6 Number of unique values in Aman Investment Amount - 35 Number of unique values in Aman Investment Equity - 40 Number of unique values in Aman Debt Amount - 11 Number of unique values in Peyush Investment Amount - 29 Number of unique values in Peyush Investment Equity - 35 Number of unique values in Peyush Debt Amount - 7 Number of unique values in Amit Investment Amount - 19 Number of unique values in Amit Investment Equity - 17 Number of unique values in Amit Debt Amount - 5 Number of unique values in Ashneer Investment Amount - 9 Number of unique values in Ashneer Investment Equity - 14 Number of unique values in Ashneer Debt Amount - 2 Number of unique values in Guest Investment Amount - 22 Number of unique values in Guest Investment Equity - 23 Number of unique values in Guest Debt Amount - 5 Number of unique values in Invested Guest Name - 9 Number of unique values in All Guest Names - 8 Number of unique values in Namita Present - 1 Number of unique values in Vineeta Present - 1 Number of unique values in Anupam Present - 1 Number of unique values in Aman Present - 1 Number of unique values in Peyush Present - 1 Number of unique values in Amit Present - 1 Number of unique values in Ashneer Present - 1 Number of unique values in Guest Present - 2
shark_tank_season1 = shark_tank.loc[shark_tank['Season Number']==1]
shark_tank_season1_without_unseen = shark_tank.loc[(shark_tank['Season Number']==1) & (shark_tank['Episode Number']!=0)]
shark_tank_season2 = shark_tank.loc[shark_tank['Season Number']==2]
shark_tank_season3 = shark_tank.loc[(shark_tank['Season Number']==3) | (shark_tank['Season Number'].isnull())]
# Data set information
print(shark_tank['Season Number'].max(), "total seasons in Indian SharkTank \n")
print(shark_tank['Pitch Number'].max(), "#startups came for pitching \n")
print("In Season 1, in", shark_tank_season1['Episode Number'].max(), "episodes, there were", shark_tank_season1.loc[shark_tank_season1['Episode Number']!=0]['Startup Name'].count(), "(real) pitches and", shark_tank_season1.loc[shark_tank_season1['Episode Number']==0]['Startup Name'].count(),"unseen pitches\n")
print("In Season 2, in", shark_tank_season2['Episode Number'].max(), "episodes, there were", shark_tank_season2.loc[shark_tank_season2['Episode Number']!=0]['Startup Name'].count(), "(real) pitches and", shark_tank_season2.loc[shark_tank_season2['Episode Number']==0]['Startup Name'].count(),"unseen pitch\n")
print("In Season 3, in", shark_tank_season3['Episode Number'].max(), "episodes, there were", shark_tank_season3.loc[shark_tank_season3['Episode Number']!=0]['Startup Name'].count(), "(real) pitches\n")
3 total seasons in Indian SharkTank 441 #startups came for pitching In Season 1, in 36 episodes, there were 122 (real) pitches and 30 unseen pitches In Season 2, in 51 episodes, there were 168 (real) pitches and 1 unseen pitch In Season 3, in 40 episodes, there were 120 (real) pitches
# Season-wise number of episodes
pd.pivot_table(shark_tank, values='Episode Number', columns='Season Number', aggfunc='max')
| Season Number | 1 | 2 | 3 |
|---|---|---|---|
| Episode Number | 36 | 51 | 40 |
# There were 2 to 4 pitches, in each episode
print(shark_tank.loc[shark_tank['Episode Number']!=0][['Season Number','Episode Number']].value_counts().sort_values(ascending=True).unique())
[2 3 4]
import pandas as pd
import matplotlib.pyplot as plt
# Load the data
data = pd.read_csv("shark Tank India.csv")
# Group by industry and count occurrences
industry_counts = data['Industry'].value_counts()
# Plot the graph
plt.figure(figsize=(10, 6))
industry_counts.plot(kind='bar', color='skyblue')
plt.title('Types of Industries Came for Investment in Shark Tank India')
plt.xlabel('Industry')
plt.ylabel('Number of Appearances')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
import pandas as pd
import matplotlib.pyplot as plt
# Load the CSV data into a DataFrame
df = pd.read_csv('shark Tank India.csv')
# Assuming the CSV file has a column named 'Industry' which contains types of industries
# You can adjust the column name according to your CSV structure
# Count the occurrences of each industry
industry_counts = df['Industry'].value_counts()
# Plotting
plt.figure(figsize=(10, 6))
industry_counts.plot(kind='bar', color='skyblue')
plt.title('Types of Industries in Shark Tank India (Latest Season)')
plt.xlabel('Industry')
plt.ylabel('Number of Investments')
plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better visibility
plt.tight_layout() # Adjust layout to prevent clipping of labels
plt.show()
# Gender wise
print("Total pitchers -", int(shark_tank['Number of Presenters'].sum()), "\n")
print("")
print("Total male pitchers -", int(shark_tank['Male Presenters'].sum()), "\n")
print("Total female pitchers -", int(shark_tank['Female Presenters'].sum()), "\n")
print("Total transgender pitchers -", int(shark_tank['Transgender Presenters'].sum()), "\n")
print("")
print("COVID entrepreneurs/startups - ", shark_tank.loc[shark_tank['Started in']==2020]['Startup Name'].count(), sep='')
Total pitchers - 665 Total male pitchers - 484 Total female pitchers - 178 Total transgender pitchers - 3 COVID entrepreneurs/startups - 31
print("Male entrepreneurs percentage - ", round(shark_tank['Male Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), "%\n", sep='')
print("Female entrepreneurs percentage - ", round(shark_tank['Female Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), "%\n", sep='')
print("Transgender entrepreneurs percentage - ", round(shark_tank['Transgender Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), "%\n", sep='')
print("Couple entrepreneurs percentage - ", round(shark_tank.loc[shark_tank['Couple Presenters']==1]['Couple Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 0), "%\n", sep='')
print("")
fig = plt.figure(figsize =(10, 7))
plt.title("Pitchers Gender wise percentage")
plt.pie([round(shark_tank['Male Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), round(shark_tank['Female Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), round(shark_tank['Transgender Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2)], labels = ["Male","Female","Transgender"], autopct='%.1f%%', colors=["lightblue", "pink", "gray"], textprops={'fontsize': 14})
plt.show()
Male entrepreneurs percentage - 72.78% Female entrepreneurs percentage - 26.77% Transgender entrepreneurs percentage - 0.45% Couple entrepreneurs percentage - 9.0%
# Age wise
print(shark_tank['Pitchers Average Age'].value_counts(),"\n")
# In percentage
print(round(shark_tank['Pitchers Average Age'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False),"\n")
plt.title("Pitchers Age wise percentage")
shark_tank["Pitchers Average Age"].value_counts().plot(kind='pie', autopct='%.0f%%', cmap='tab20c', fontsize=14)
plt.ylabel('')
Pitchers Average Age Middle 231 Young 88 Old 1 Name: count, dtype: int64 Pitchers Average Age Middle 72% Young 28% Old 0% Name: proportion, dtype: object
Text(0, 0.5, '')
# Offers received
print(shark_tank['Received Offer'].value_counts(), "\n")
print(round(shark_tank['Received Offer'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False))
plt.figure(figsize = (15,9))
ax1 = plt.subplot(221)
shark_tank["Received Offer"].value_counts().plot(kind='bar', color=["limegreen","crimson"], ec="k")
plt.xlabel("Number of Offers Received / Not Received")
plt.yticks([])
plt.xticks(rotation=0)
for x,y in enumerate(shark_tank["Received Offer"].value_counts()):
plt.annotate(y, (x,y), fontsize=13, color="blue")
ax2 = plt.subplot(222)
shark_tank["Received Offer"].value_counts().plot(kind='pie', autopct='%.0f%%', explode = (0,0.05), colors=["limegreen","crimson"], shadow=True, fontsize=13)
plt.ylabel('')
# 272 companies received offers & 124 startups could not convince #Sharks to invest.
Received Offer 1 216 0 104 Name: count, dtype: int64 Received Offer 1 68% 0 32% Name: proportion, dtype: object
Text(0, 0.5, '')
# Offers accepted
print(shark_tank['Accepted Offer'].value_counts(), "\n")
print(round(shark_tank['Accepted Offer'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False))
plt.figure(figsize = (15, 9))
ax1 = plt.subplot(221)
shark_tank["Accepted Offer"].value_counts().plot(kind='bar', color=["limegreen","crimson"], ec="k")
plt.xlabel("Number of Offers Accepted / Rejected")
plt.yticks([])
plt.xticks(rotation = 0)
for x,y in enumerate(shark_tank["Accepted Offer"].value_counts()):
plt.annotate(y, (x,y), fontsize=13, color="blue")
ax2 = plt.subplot(222)
shark_tank["Accepted Offer"].value_counts().plot(kind='pie', autopct='%.0f', explode = (0,0.05), colors=["limegreen","crimson"], shadow=True, fontsize=13)
plt.ylabel('')
# 220 companies accepted investments they got & 52 #Startups did not accept Shark's offer.
Accepted Offer 1.0 176 0.0 40 Name: count, dtype: int64 Accepted Offer 1.0 81% 0.0 19% Name: proportion, dtype: object
Text(0, 0.5, '')
# Offers rejected by pitchers/startup companies
print(shark_tank[shark_tank['Accepted Offer']==0]["Startup Name"].count())
shark_tank.loc[shark_tank['Accepted Offer']==0, ["Season Number","Startup Name","Industry","Original Ask Amount","Original Offered Equity"]]
40
| Season Number | Startup Name | Industry | Original Ask Amount | Original Offered Equity | |
|---|---|---|---|---|---|
| 6 | 1 | qZenseLabs | Food | 100.0 | 0.25 |
| 19 | 1 | Torch-it | Education | 75.0 | 1.00 |
| 21 | 1 | LaKheerDeli | Food | 50.0 | 7.50 |
| 27 | 1 | KabiraHandmad | Food | 100.0 | 5.00 |
| 41 | 1 | MorrikoPureFoods | Food | 100.0 | 3.00 |
| 55 | 1 | IndiaHempandCo | Food | 50.0 | 4.00 |
| 60 | 1 | KetoIndia | Food | 150.0 | 1.25 |
| 70 | 1 | Moonshine | Food | 80.0 | 0.50 |
| 71 | 1 | Falhari | Food | 50.0 | 2.00 |
| 73 | 1 | UrbanMonkey | Beauty/Fashion | 100.0 | 1.00 |
| 74 | 1 | GuardianGears | Manufacturing | 30.0 | 5.00 |
| 81 | 1 | Alpino | Food | 150.0 | 2.00 |
| 87 | 1 | AlisteTechnologies | Technology/Software | 60.0 | 5.00 |
| 93 | 1 | PDDFalcon | Manufacturing | 75.0 | 3.00 |
| 94 | 1 | PlayBoxTV | Services | 100.0 | 3.50 |
| 104 | 1 | ExperentialEtc | Technology/Software | 200.0 | 4.00 |
| 106 | 1 | C3Med-Tech | Medical/Health | 35.0 | 6.00 |
| 113 | 1 | GreenProtein | Food | 60.0 | 2.00 |
| 116 | 1 | Woloo | Technology/Software | 50.0 | 4.00 |
| 119 | 1 | FrenchCrown | Beauty/Fashion | 150.0 | 0.33 |
| 121 | 1 | Devnagri | Technology/Software | 100.0 | 1.00 |
| 131 | 1 | Scintiglo | Medical/Health | 75.0 | 1.00 |
| 135 | 1 | UrbanNaps | Services | 50.0 | 4.00 |
| 138 | 1 | Picsniff | Technology/Software | 55.0 | 1.00 |
| 149 | 1 | Artment | Beauty/Fashion | 170.0 | 2.50 |
| 151 | 1 | Eume | Beauty/Fashion | 150.0 | 2.00 |
| 158 | 2 | ATMOSPHERE | Food | 75.0 | 3.00 |
| 165 | 2 | Flatheads | Beauty/Fashion | 75.0 | 3.00 |
| 189 | 2 | Diabexy | Food | 150.0 | 1.00 |
| 199 | 2 | AvimeeHerbal | Beauty/Fashion | 280.0 | 0.50 |
| 206 | 2 | PMV | Vehicles/Electrical Vehicles | 100.0 | 1.00 |
| 212 | 2 | CheeseCake&Co. | Food | 100.0 | 2.00 |
| 215 | 2 | BeUnic | Services | 100.0 | 10.00 |
| 229 | 2 | GavinParis | Beauty/Fashion | 50.0 | 5.00 |
| 233 | 2 | HobbyIndia | Furnishing/Household | 50.0 | 3.00 |
| 237 | 2 | DesiToys | Manufacturing | 50.0 | 3.00 |
| 245 | 2 | Tipayi | Manufacturing | 50.0 | 10.00 |
| 256 | 2 | MidNightAngelsByPC | Beauty/Fashion | 75.0 | 6.00 |
| 278 | 2 | TwistingScoops | Food | 100.0 | 2.50 |
| 316 | 2 | GODESi | Food | 90.0 | 0.50 |
# Maximum amount requested
print("Maximum amount requested, by a pitcher - Rs.", round(shark_tank["Original Ask Amount"].max()/100), "crores")
Maximum amount requested, by a pitcher - Rs. 300 crores
# Least amount requested
print("Least amount requested, by a pitcher - Rs.", round(shark_tank["Original Ask Amount"].min()*100000))
Least amount requested, by a pitcher - Rs. 0
# Sum of investment amount asked, in Shark Tank, in India
print("Sum of investment amount asked, by all startup companies, in Indian Shark Tank -", format_currency(shark_tank['Original Ask Amount'].sum()/100, 'INR', locale='en_IN').replace(".00", ""), "crores")
Sum of investment amount asked, by all startup companies, in Indian Shark Tank - ₹540.41 crores
# Amount invested by all sharks, in India SharkTank
print("Amount invested by all sharks, in Shark Tank India -", format_currency(shark_tank['Total Deal Amount'].sum()/100, 'INR', locale='en_IN').replace(".00", ""), "crores")
Amount invested by all sharks, in Shark Tank India - ₹110.06 crores
# Sum of loan/debt amount, in India Shark Tank
print("Sum of loan/debt amount, given by all sharks, in India SharkTank -", format_currency(shark_tank['Total Deal Debt'].sum()/100, 'INR', locale='en_IN').replace(".00", ""), "crores")
Sum of loan/debt amount, given by all sharks, in India SharkTank - ₹18.11 crores
# Top 20 investments (more than 1Cr), as per total investment/deal amount (in lakhs)
print(shark_tank.groupby('Startup Name')['Total Deal Amount'].max().nlargest(20))
tmpdf = shark_tank.sort_values('Total Deal Amount', ascending=False)[0:20]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Amount', color="Startup Name", title="Highest Investment as per deal amount (in lakhs)", text=tmpdf['Total Deal Amount'])
fig.show()
Startup Name MeduLance 200.0 Pharmallama 200.0 UnStop 200.0 AasVidyalaya 150.0 Portl 150.0 Snitch 150.0 Stage 150.0 Trunome 150.0 MindPeers 106.0 Annie 105.0 Broomees 100.0 GearHeadMotors 100.0 Geeani 100.0 Get-A-Whey 100.0 HammerLifestyle 100.0 Haqdarshak 100.0 Hoovu 100.0 HumpyA2 100.0 INACAN 100.0 InsuranceSamadhan 100.0 Name: Total Deal Amount, dtype: float64
# Top 20 investments, as per total equity/shares percentage diluted
print(shark_tank.groupby('Startup Name')['Total Deal Equity'].max().nlargest(20))
tmpdf = shark_tank.sort_values('Total Deal Equity', ascending=False)[0:20]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Equity', color="Startup Name", title="Highest Investment as per Equity percentage", text=tmpdf['Total Deal Equity'].map(int).map(str) + "%")
fig.show()
Startup Name Sid07Designs 75.00 BoozScooters 50.00 IsakFragrances 50.00 HammerLifestyle 40.00 KGAgrotech 40.00 TheSassBar 35.00 VivalyfInnovations 33.33 GoldSafeSolutions 30.00 HeartUpMySleeves 30.00 JainShikanji 30.00 ColourMeMad-CMM 25.00 CosIQ 25.00 FindYourKicksIndia 25.00 HoloKitab 25.00 PNT 25.00 Raasa 25.00 LOKA 24.00 TheQuirkyNaari 24.00 WakaoFoods 21.00 Angrakhaa 20.00 Name: Total Deal Equity, dtype: float64
# Startups who sold more than 1/3rd of their company (equity) to Sharks
print(shark_tank.loc[shark_tank['Total Deal Equity'] > 32 ][["Startup Name"]].count())
print(shark_tank.loc[shark_tank['Total Deal Equity'] > 32 ][["Season Number","Startup Name","Total Deal Amount", "Total Deal Equity"]])
tmpdf = shark_tank.loc[shark_tank['Total Deal Equity'] > 32 ].sort_values('Total Deal Equity', ascending=False)
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Equity', color="Startup Name", title="Startups who sold more than 1/3rd of their company", text=tmpdf['Total Deal Equity'].map(int).map(str) + "%")
fig.show()
Startup Name 7
dtype: int64
Season Number Startup Name Total Deal Amount Total Deal Equity
1 1 BoozScooters 40.0 50.00
23 1 VivalyfInnovations 56.0 33.33
43 1 HammerLifestyle 100.0 40.00
66 1 Sid07Designs 25.0 75.00
76 1 TheSassBar 50.0 35.00
77 1 KGAgrotech 10.0 40.00
82 1 IsakFragrances 50.0 50.00
# Top 20 investments, as per total debt/loan amount
print(shark_tank.groupby('Startup Name')['Total Deal Debt'].max().nlargest(20))
tmpdf = shark_tank.sort_values('Total Deal Debt', ascending=False)[0:20]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Debt', color="Startup Name", title="Highest Investment as per Debt amount (in lakhs)", text=tmpdf['Total Deal Debt'])
fig.show()
Startup Name Stage 150.0 WatchoutWearables 100.0 uBreathe 100.0 Otua 99.0 Wol3D 70.0 TAC 69.0 maisha 65.0 Hood 60.0 iMumz 60.0 AyuSynk 50.0 DailyDump 50.0 Freebowler 50.0 GROWiT 50.0 LilGoodness 50.0 NamhyaFoods 50.0 NutriCook 50.0 Rubans 50.0 StoreMyGoods 50.0 Aadvik 45.0 VSMani 41.0 Name: Total Deal Debt, dtype: float64
# Startups who got Debt/loan amount
print("Number of startups who got debt/loan amount", shark_tank['Total Deal Debt'].count(),"\n")
shark_tank.loc[shark_tank['Total Deal Debt'] > 0][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity","Total Deal Debt"]]
Number of startups who got debt/loan amount 39
| Season Number | Startup Name | Total Deal Amount | Total Deal Equity | Total Deal Debt | |
|---|---|---|---|---|---|
| 8 | 1 | NOCD | 20.0 | 15.00 | 30.0 |
| 44 | 1 | PNT | 25.0 | 25.00 | 25.0 |
| 46 | 1 | BambooIndia | 50.0 | 3.50 | 30.0 |
| 56 | 1 | Otua | 1.0 | 1.00 | 99.0 |
| 62 | 1 | TheStatePlate | 40.0 | 3.00 | 25.0 |
| 66 | 1 | Sid07Designs | 25.0 | 75.00 | 22.0 |
| 72 | 1 | NamhyaFoods | 50.0 | 10.00 | 50.0 |
| 77 | 1 | KGAgrotech | 10.0 | 40.00 | 20.0 |
| 120 | 1 | StoreMyGoods | 50.0 | 4.00 | 50.0 |
| 156 | 2 | WatchoutWearables | 100.0 | 10.00 | 100.0 |
| 157 | 2 | SoupX | 50.0 | 18.00 | 25.0 |
| 159 | 2 | Stage | 150.0 | 0.60 | 150.0 |
| 163 | 2 | Brandsdaddy | 35.0 | 5.00 | 35.0 |
| 172 | 2 | AyuSynk | 50.0 | 3.50 | 50.0 |
| 180 | 2 | Freebowler | 25.0 | 7.50 | 50.0 |
| 183 | 2 | DailyDump | 30.0 | 4.00 | 50.0 |
| 196 | 2 | VSMani | 19.0 | 1.00 | 41.0 |
| 201 | 2 | ekatra | 20.0 | 20.00 | 20.0 |
| 204 | 2 | licksters | 25.0 | 5.00 | 25.0 |
| 213 | 2 | Dabble | 15.0 | 10.00 | 35.0 |
| 218 | 2 | HoneyVeda | 50.0 | 20.00 | 25.0 |
| 220 | 2 | SwadeshiBlessings | 25.0 | 5.00 | 25.0 |
| 231 | 2 | BlueTea | 50.0 | 3.00 | 25.0 |
| 248 | 2 | Pabiben | 10.0 | 5.00 | 40.0 |
| 249 | 2 | Homestrap | 50.0 | 7.00 | 20.0 |
| 250 | 2 | uBreathe | 50.0 | 5.00 | 100.0 |
| 252 | 2 | iMumz | 10.0 | 1.00 | 60.0 |
| 254 | 2 | Freakins | 50.0 | 2.50 | 20.0 |
| 277 | 2 | Hood | 60.0 | 0.54 | 60.0 |
| 279 | 2 | GROWiT | 50.0 | 1.00 | 50.0 |
| 282 | 2 | Wol3D | 80.0 | 2.00 | 70.0 |
| 290 | 2 | Aadvik | 15.0 | 1.50 | 45.0 |
| 296 | 2 | NutriCook | 50.0 | 10.00 | 50.0 |
| 297 | 2 | Subhag | 20.0 | 1.00 | 30.0 |
| 306 | 2 | Rubans | 100.0 | 1.00 | 50.0 |
| 309 | 2 | LilGoodness | 50.0 | 1.00 | 50.0 |
| 313 | 2 | maisha | 10.0 | 1.00 | 65.0 |
| 317 | 2 | TAC | 81.0 | 1.00 | 69.0 |
| 318 | 2 | Naara-Aaba | 50.0 | 5.00 | 25.0 |
# Startups who gave Royalty
print("Number of startups who gave Royalty", shark_tank['Royalty Deal'].count(),"\n")
shark_tank.loc[shark_tank['Royalty Deal'] == 1][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity"]]
Number of startups who gave Royalty 14
| Season Number | Startup Name | Total Deal Amount | Total Deal Equity | |
|---|---|---|---|---|
| 321 | 3 | HonestHome | 100.0 | 3.00 |
| 322 | 3 | AdilQadri | 100.0 | 1.00 |
| 342 | 3 | Tiggle | 50.0 | 20.00 |
| 351 | 3 | GudGum | 80.0 | 10.00 |
| 362 | 3 | DecodeAge | 100.0 | 2.25 |
| 375 | 3 | YesMadam | 150.0 | 2.00 |
| 378 | 3 | PushSports | 80.0 | 4.00 |
| 384 | 3 | D'chica | 80.0 | 2.00 |
| 385 | 3 | Refit | 200.0 | 1.00 |
| 387 | 3 | Artinci | 50.0 | 5.00 |
| 397 | 3 | Cosmix | 100.0 | 1.00 |
| 401 | 3 | UnclePetersPanCakes | 60.0 | 3.00 |
| 410 | 3 | KryzenBiotech | 75.0 | 15.00 |
| 433 | 3 | AToddlerThing | 40.0 | 2.00 |
# Startups who gave Advisory shares
print("Number of startups who gave Advisory shares/equity", shark_tank['Advisory Shares Equity'].count(),"\n")
shark_tank.loc[shark_tank['Advisory Shares Equity'] > 0][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity", "Advisory Shares Equity"]]
# DATA INCOMPLETE
Number of startups who gave Advisory shares/equity 3
| Season Number | Startup Name | Total Deal Amount | Total Deal Equity | Advisory Shares Equity | |
|---|---|---|---|---|---|
| 334 | 3 | AIKavach/Panoplia | 100.0 | 2.50 | 2.5 |
| 341 | 3 | WeHear | 250.0 | 1.00 | 1.5 |
| 349 | 3 | Arata | 100.0 | 1.33 | 0.6 |
# Deals with conditions
print("Number of startups who accepted for conditional deals", shark_tank['Deal Has Conditions'].count(),"\n")
shark_tank.loc[shark_tank['Deal Has Conditions'] == 'yes'][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity", "Advisory Shares Equity"]]
Number of startups who accepted for conditional deals 26
| Season Number | Startup Name | Total Deal Amount | Total Deal Equity | Advisory Shares Equity | |
|---|---|---|---|---|---|
| 8 | 1 | NOCD | 20.0 | 15.00 | NaN |
| 29 | 1 | Meatyour | 30.0 | 20.00 | NaN |
| 32 | 1 | ARRCOATSurfaceTextures | 50.0 | 15.00 | NaN |
| 44 | 1 | PNTRobotics | 25.0 | 25.00 | NaN |
| 79 | 1 | PawsIndia | 50.0 | 15.00 | NaN |
| 82 | 1 | IsakFragrances | 50.0 | 50.00 | NaN |
| 105 | 1 | GrowFitter | 50.0 | 2.00 | NaN |
| 224 | 2 | Amore | 75.0 | 7.50 | NaN |
| 238 | 2 | CloudWorx | 40.0 | 3.20 | NaN |
| 242 | 2 | Daryaganj | 90.0 | 1.00 | NaN |
| 243 | 2 | DhruvVidyut | 0.0 | 0.50 | NaN |
| 254 | 2 | Freakins | 50.0 | 2.50 | NaN |
| 264 | 2 | HoloKitab | 45.0 | 25.00 | NaN |
| 266 | 2 | Hornback | 50.0 | 2.50 | NaN |
| 298 | 2 | SinghStyled | 50.0 | 10.00 | NaN |
| 334 | 3 | AIKavach/Panoplia | 100.0 | 2.50 | 2.5 |
| 341 | 3 | WeHear | 250.0 | 1.00 | 1.5 |
| 349 | 3 | Arata | 100.0 | 1.33 | 0.6 |
| 358 | 3 | DaakRoom | 36.0 | 6.00 | NaN |
| 379 | 3 | ORBO | 100.0 | 1.00 | NaN |
| 387 | 3 | Artinci | 50.0 | 5.00 | NaN |
| 393 | 3 | AristaVault | 20.0 | 1.00 | NaN |
| 403 | 3 | CandidMen | 60.0 | 5.00 | NaN |
| 416 | 3 | MEPACK | 7.0 | 10.00 | NaN |
| 436 | 3 | iDreamCareer | 60.0 | 1.00 | NaN |
| 440 | 3 | Smotect | 50.0 | 5.00 | NaN |
# Amount Invested by sharks, in all seasons
Amount = [shark_tank['Ashneer Investment Amount'].sum(), shark_tank['Namita Investment Amount'].sum(), shark_tank['Anupam Investment Amount'].sum(), shark_tank['Vineeta Investment Amount'].sum(),
shark_tank['Aman Investment Amount'].sum(), shark_tank['Peyush Investment Amount'].sum(), shark_tank['Guest Investment Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush', 'Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in all seasons", fontsize=14)
plt.show()
# Aman invested maximum amount, in all seasons - 35 crores
# Ashneer invested minimum amount, in all seasons - 5 crores
# Amount Invested by sharks
# Season 1
Amount = [shark_tank_season1['Ashneer Investment Amount'].sum(), shark_tank_season1['Namita Investment Amount'].sum(), shark_tank_season1['Anupam Investment Amount'].sum(), shark_tank_season1['Vineeta Investment Amount'].sum(),
shark_tank_season1['Aman Investment Amount'].sum(), shark_tank_season1['Peyush Investment Amount'].sum(), shark_tank_season1['Guest Investment Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in Season 1", fontsize=14)
plt.show()
# Season 2
Amount = [shark_tank_season2['Namita Investment Amount'].sum(), shark_tank_season2['Anupam Investment Amount'].sum(), shark_tank_season2['Vineeta Investment Amount'].sum(),
shark_tank_season2['Aman Investment Amount'].sum(), shark_tank_season2['Peyush Investment Amount'].sum(), shark_tank_season2['Amit Investment Amount'].sum(), shark_tank_season2['Guest Investment Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in Season 2", fontsize=14)
plt.show()
# Season 3
Amount = [shark_tank_season3['Namita Investment Amount'].sum(), shark_tank_season3['Anupam Investment Amount'].sum(), shark_tank_season3['Vineeta Investment Amount'].sum(),
shark_tank_season3['Aman Investment Amount'].sum(), shark_tank_season3['Peyush Investment Amount'].sum(), shark_tank_season3['Amit Investment Amount'].sum(), shark_tank_season3['Guest Investment Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in Season 3", fontsize=14)
plt.show()
# Equity received by sharks, in all seasons
Amount = [shark_tank['Ashneer Investment Equity'].sum(), shark_tank['Namita Investment Equity'].sum(), shark_tank['Anupam Investment Equity'].sum(), shark_tank['Vineeta Investment Equity'].sum(),
shark_tank['Aman Investment Equity'].sum(), shark_tank['Peyush Investment Equity'].sum(), shark_tank['Guest Investment Equity'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush', 'Guests']
df = {'Name':name, 'Total Equity':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total equity received (in %), by Sharks, in all companies, in all seasons", fontsize=15)
plt.show()
# Peyush got maximum equity of - 494% in different companies, in all seasons
# Ashneer got minimum equity of - 93% in different companies, in all seasons
# Equity received by sharks
# Season 1
Equity = [shark_tank_season1['Ashneer Investment Equity'].sum(), shark_tank_season1['Namita Investment Equity'].sum(), shark_tank_season1['Anupam Investment Equity'].sum(), shark_tank_season1['Vineeta Investment Equity'].sum(),
shark_tank_season1['Aman Investment Equity'].sum(), shark_tank_season1['Peyush Investment Equity'].sum(), shark_tank_season1['Guest Investment Equity'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Equity':Equity}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Equity):
plt.text(x=index, y =d+1, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received (in %) by Sharks, in all companies, in Season 1", fontsize=14)
plt.show()
# Season 2
Equity = [shark_tank_season2['Namita Investment Equity'].sum(), shark_tank_season2['Anupam Investment Equity'].sum(), shark_tank_season2['Vineeta Investment Equity'].sum(),
shark_tank_season2['Aman Investment Equity'].sum(), shark_tank_season2['Peyush Investment Equity'].sum(), shark_tank_season2['Amit Investment Equity'].sum(), shark_tank_season2['Guest Investment Equity'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total Equity':Equity}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Equity):
plt.text(x=index, y =d+1, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received (in %) by Sharks, in all companies, in Season 2", fontsize=14)
plt.show()
# Season 3
Equity = [shark_tank_season3['Namita Investment Equity'].sum(), shark_tank_season3['Anupam Investment Equity'].sum(), shark_tank_season3['Vineeta Investment Equity'].sum(),
shark_tank_season3['Aman Investment Equity'].sum(), shark_tank_season3['Peyush Investment Equity'].sum(), shark_tank_season3['Amit Investment Equity'].sum(), shark_tank_season3['Guest Investment Equity'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total Equity':Equity}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Equity):
plt.text(x=index, y =d+1, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received (in %) by Sharks, in all companies, in Season 3", fontsize=14)
plt.show()
# Investment based on the Debt/loaned Amount, in all seasons
Amount = [shark_tank['Ashneer Debt Amount'].sum(), shark_tank['Namita Debt Amount'].sum(), shark_tank['Anupam Debt Amount'].sum(), shark_tank['Vineeta Debt Amount'].sum(),
shark_tank['Aman Debt Amount'].sum(), shark_tank['Peyush Debt Amount'].sum(), shark_tank['Guest Debt Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush', 'Guests']
df = {'Name':name, 'Total Equity':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in all seasons", fontsize=15)
plt.show()
# Namita gave maximum debt amount, in all seasons - 5 crores
# All guests gave minimum debt amount, in all seasons - 0.87 crores
# Investment based on the Debt/loaned Amount
# Season 1
debt = [shark_tank_season1['Ashneer Debt Amount'].sum(), shark_tank_season1['Namita Debt Amount'].sum(), shark_tank_season1['Anupam Debt Amount'].sum(), shark_tank_season1['Vineeta Debt Amount'].sum(),
shark_tank_season1['Aman Debt Amount'].sum(), shark_tank_season1['Peyush Debt Amount'].sum(), shark_tank_season1['Guest Debt Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total debt':debt}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total debt'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(debt):
plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in Season 1", fontsize=14)
plt.show()
# Season 2
debt = [shark_tank_season2['Namita Debt Amount'].sum(), shark_tank_season2['Anupam Debt Amount'].sum(), shark_tank_season2['Vineeta Debt Amount'].sum(),
shark_tank_season2['Aman Debt Amount'].sum(), shark_tank_season2['Peyush Debt Amount'].sum(), shark_tank_season2['Amit Debt Amount'].sum(), shark_tank_season2['Guest Debt Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total debt':debt}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total debt'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(debt):
plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in Season 2", fontsize=14)
plt.show()
# Season 3
debt = [shark_tank_season3['Namita Debt Amount'].sum(), shark_tank_season3['Anupam Debt Amount'].sum(), shark_tank_season3['Vineeta Debt Amount'].sum(),
shark_tank_season3['Aman Debt Amount'].sum(), shark_tank_season3['Peyush Debt Amount'].sum(), shark_tank_season3['Amit Debt Amount'].sum(), shark_tank_season3['Guest Debt Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total debt':debt}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total debt'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(debt):
plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in Season 3", fontsize=14)
plt.show()
print("Namita gave 20% higher loan in season 2, compared to season 1\nAman started giving more debt/loan in season 3")
Namita gave 20% higher loan in season 2, compared to season 1 Aman started giving more debt/loan in season 3
# Number of companies invested, in all seasons
Amount = [shark_tank['Ashneer Investment Amount'].count(), shark_tank['Namita Investment Amount'].count(), shark_tank['Anupam Investment Amount'].count(), shark_tank['Vineeta Investment Amount'].count(),
shark_tank['Aman Investment Amount'].count(), shark_tank['Peyush Investment Amount'].count(), shark_tank['Guest Investment Amount'].count()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in all seasons", fontsize=14)
plt.show()
# Number of companies invested
# Season 1
Amount = [shark_tank_season1['Ashneer Investment Amount'].count(), shark_tank_season1['Namita Investment Amount'].count(), shark_tank_season1['Anupam Investment Amount'].count(), shark_tank_season1['Vineeta Investment Amount'].count(),
shark_tank_season1['Aman Investment Amount'].count(), shark_tank_season1['Peyush Investment Amount'].count(), shark_tank_season1['Guest Investment Amount'].count()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in Season 1", fontsize=14)
plt.show()
# Season 2
Amount = [shark_tank_season2['Namita Investment Amount'].count(), shark_tank_season2['Anupam Investment Amount'].count(), shark_tank_season2['Vineeta Investment Amount'].count(),
shark_tank_season2['Aman Investment Amount'].count(), shark_tank_season2['Peyush Investment Amount'].count(), shark_tank_season2['Amit Investment Amount'].count(), shark_tank_season2['Guest Investment Amount'].count()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in Season 2", fontsize=14)
plt.show()
# Season 3
Amount = [shark_tank_season3['Namita Investment Amount'].count(), shark_tank_season3['Anupam Investment Amount'].count(), shark_tank_season3['Vineeta Investment Amount'].count(),
shark_tank_season3['Aman Investment Amount'].count(), shark_tank_season3['Peyush Investment Amount'].count(), shark_tank_season3['Amit Investment Amount'].count(), shark_tank_season3['Guest Investment Amount'].count()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in Season 3", fontsize=14)
plt.show()
# Word cloud based on Startup Names, in all seasons
text = " Shark Tank India ".join(cat for cat in shark_tank['Startup Name'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='black', colormap='Set2', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(25,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
# Word cloud based on Startup Names, in current/latest season (3rd season)
text = " Shark Tank India ".join(cat for cat in shark_tank_season3['Startup Name'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=1800, height=1300, stopwords=stop_words, background_color='black', colormap='Set3', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(14,14))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
print("Total investments by Ashneer", shark_tank[shark_tank['Ashneer Investment Amount']>=0][['Ashneer Investment Amount']].count().to_string()[-2:])
print("Investment amount by Ashneer", round(shark_tank['Ashneer Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Ashneer", round(shark_tank['Ashneer Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Ashneer", round(shark_tank['Ashneer Debt Amount'].sum()/100, 2), "crores\n")
print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Ashneer Investment Amount']>=0][["Startup Name","Industry","Ashneer Investment Amount"]].to_string(index=False))
print('-'*75)
print("\nAshneer industry wise investments\n")
print(shark_tank[shark_tank['Ashneer Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Ashneer Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Ashneer Investment Amount']>=0] [["Startup Name","Ashneer Investment Amount","Ashneer Investment Equity"]].sort_values(by="Ashneer Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Ashneer Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Ashneer 21
Investment amount by Ashneer 5.39 crores
Equity received by Ashneer 93.24 % in different companies
Debt/loan amount by Ashneer 1.14 crores
Company details:
---------------------------------------------------------------------------
Startup Name Industry Ashneer Investment Amount
BluePineFoods Food 25.00
BoozScooters Vehicles/Electrical Vehicles 20.00
TagzFoods Food 70.00
SkippiIcePops Food 20.00
RaisingSuperstars Education 50.00
BeyondSnack Food 25.00
MotionBreeze Vehicles/Electrical Vehicles 30.00
EventBeep Education 10.00
TheYarnBazaar Manufacturing 25.00
BambooIndia Manufacturing 25.00
FindYourKicksIndia Beauty/Fashion 10.00
AasVidyalaya Education 50.00
Otua Vehicles/Electrical Vehicles 1.00
WeSTOCK Animal/Pets 15.00
INACAN Food 20.00
Get-A-Whey Food 33.33
HairOriginals Beauty/Fashion 20.00
TweekLabs Sports 20.00
Proxgy Technology/Software 50.00
NomadFoodProject Food 10.00
JainShikanji Food 10.00
---------------------------------------------------------------------------
Ashneer industry wise investments
Industry
Food 8
Vehicles/Electrical Vehicles 3
Education 3
Manufacturing 2
Beauty/Fashion 2
Animal/Pets 1
Sports 1
Technology/Software 1
Name: count, dtype: int64
print("Total investments by Namita", shark_tank[shark_tank['Namita Investment Amount']>0][['Namita Investment Amount']].count().to_string()[-2:])
print("Investment amount by Namita", round(shark_tank['Namita Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Namita", round(shark_tank['Namita Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Namita", round(shark_tank['Namita Debt Amount'].sum()/100, 2), "crores\n")
print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Namita Investment Amount']>0][["Startup Name","Industry","Namita Investment Amount"]].to_string(index=False))
print('-'*75)
print("\nNamita industry wise investments\n")
print(shark_tank[shark_tank['Namita Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Namita Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Namita Investment Amount']>0] [["Startup Name","Namita Investment Amount","Namita Investment Equity"]].sort_values(by="Namita Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Namita Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Namita 66
Investment amount by Namita 20.98 crores
Equity received by Namita 301.59 % in different companies
Debt/loan amount by Namita 5.25 crores
Company details:
---------------------------------------------------------------------------
Startup Name Industry Namita Investment Amount
JhaJiAchaar Food 28.300000
Bummer Beauty/Fashion 37.500000
SkippiIcePops Food 20.000000
Menstrupedia Education 50.000000
Altor Manufacturing 25.000000
Nuutjob Beauty/Fashion 8.330000
Farda Beauty/Fashion 15.000000
Auli Beauty/Fashion 75.000000
Annie Education 35.000000
TheRenalProject Medical/Health 50.000000
Cocofit Food 0.000016
BeyondWater Food 37.500000
FindYourKicksIndia Beauty/Fashion 10.000000
AasVidyalaya Education 50.000000
WeSTOCK Animal/Pets 15.000000
INACAN Food 20.000000
SunfoxTechnologies Medical/Health 20.000000
RarePlanet Manufacturing 65.000000
WattTechnovations Medical/Health 0.000253
WakaoFoods Food 25.000000
KabaddiAdda Sports 40.000000
ColourMeMad-CMM Beauty/Fashion 40.000000
NomadFoodProject Food 10.000000
SneaKare Beauty/Fashion 7.000000
StoreMyGoods Services 25.000000
VeryMuchIndian Beauty/Fashion 25.000000
Stage Entertainment 50.000000
Girgit Beauty/Fashion 20.000000
Brandsdaddy Manufacturing 35.000000
Haqdarshak Services 33.330000
AyuSynk Medical/Health 50.000000
AtypicalAdvantage Technology/Software 15.000000
Nestroots Furnishing/Household 50.000000
Freebowler Sports 25.000000
DailyDump Manufacturing 30.000000
Janitri Medical/Health 100.000000
InsideFPV Manufacturing 18.750000
Pflow Medical/Health 30.000000
VSMani Food 19.000000
SpiceStory Food 70.000000
Snitch Beauty/Fashion 30.000000
Portl Services 50.000000
Broomees Services 33.330000
PadCare Manufacturing 25.000000
SwadeshiBlessings Furnishing/Household 12.500000
UnStop Technology/Software 50.000000
CloudWorx Technology/Software 20.000000
Mahantam Manufacturing 6.000000
MindPeers Medical/Health 17.660000
DigiQure Medical/Health 40.000000
Pabiben Beauty/Fashion 10.000000
uBreathe Manufacturing 50.000000
Perfora Furnishing/Household 26.660000
MeduLance Medical/Health 66.660000
HoloKitab Technology/Software 45.000000
GladFul Food 16.660000
Pharmallama Medical/Health 40.000000
GROWiT Agriculture 25.000000
funngro Technology/Software 25.000000
LondonBubble Food 75.000000
Subhag Medical/Health 20.000000
ThePlatedProject Services 25.000000
SoulUp Services 50.000000
Rubans Beauty/Fashion 33.330000
ForeverModest Beauty/Fashion 5.000000
Sahayatha Medical/Health 20.000000
---------------------------------------------------------------------------
Namita industry wise investments
Industry
Beauty/Fashion 13
Medical/Health 12
Food 11
Manufacturing 8
Services 6
Technology/Software 5
Education 3
Furnishing/Household 3
Sports 2
Animal/Pets 1
Entertainment 1
Agriculture 1
Name: count, dtype: int64
print("Total investments by Anupam", shark_tank[shark_tank['Anupam Investment Amount']>=0][['Anupam Investment Amount']].count().to_string()[-2:])
print("Investment amount by Anupam", round(shark_tank['Anupam Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Anupam", round(shark_tank['Anupam Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Anupam", round(shark_tank['Anupam Debt Amount'].sum()/100, 2), "crores\n")
print("Company details:")
print('-'*85)
print(shark_tank.loc[shark_tank['Anupam Investment Amount']>=0][["Startup Name","Industry","Anupam Investment Amount"]].to_string(index=False))
print('-'*85)
print("\nAnupam industry wise investments\n")
print(shark_tank[shark_tank['Anupam Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Anupam Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Anupam Investment Amount']>=0] [["Startup Name","Anupam Investment Amount","Anupam Investment Equity"]].sort_values(by="Anupam Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Anupam Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Anupam 51
Investment amount by Anupam 14.51 crores
Equity received by Anupam 305.83 % in different companies
Debt/loan amount by Anupam 0.98 crores
Company details:
-------------------------------------------------------------------------------------
Startup Name Industry Anupam Investment Amount
HeartUpMySleeves Beauty/Fashion 12.500000
CosIQ Beauty/Fashion 25.000000
RevampMoto Vehicles/Electrical Vehicles 50.000000
SkippiIcePops Food 20.000000
Kavach Education 2.500000
VivalyfInnovations Medical/Health 28.000000
Meatyour Food 10.000000
ARRCOATSurfaceTextures Manufacturing 50.000000
LOKA Technology/Software 13.330000
Annie Education 35.000000
Carragreen Manufacturing 25.000000
TheYarnBazaar Manufacturing 25.000000
Cocofit Food 0.000016
BambooIndia Manufacturing 25.000000
Let'sTry Food 22.500000
FindYourKicksIndia Beauty/Fashion 10.000000
INACAN Food 20.000000
TheQuirkyNaari Beauty/Fashion 17.500000
HairOriginals Beauty/Fashion 20.000000
TheSassBar Beauty/Fashion 25.000000
PawsIndia Animal/Pets 50.000000
SunfoxTechnologies Medical/Health 20.000000
WattTechnovations Medical/Health 0.000253
TweekLabs Sports 20.000000
JainShikanji Food 10.000000
Dorji Food 10.000000
WatchoutWearables Electronics 50.000000
PatilKaki Food 20.000000
Winston Beauty/Fashion 50.000000
TeaFit Liquor/Beverages 12.500000
Zillionaire Beauty/Fashion 100.000000
Kyari Manufacturing 25.500000
Solinas Services 45.000000
Raasa Food 50.000000
Snitch Beauty/Fashion 30.000000
Ravel Beauty/Fashion 75.000000
HoneyVeda Food 25.000000
PadCare Manufacturing 25.000000
Geeani Vehicles/Electrical Vehicles 33.330000
Amore Food 75.000000
SharmaJiKiAata Food 40.000000
UnStop Technology/Software 50.000000
CloudWorx Technology/Software 20.000000
Mahantam Manufacturing 6.000000
DhruvVidyut Vehicles/Electrical Vehicles 0.000000
Homestrap Furnishing/Household 50.000000
Pharmallama Medical/Health 40.000000
Trunome Medical/Health 37.500000
What'sUpWellness Food 20.000000
ForeverModest Beauty/Fashion 5.000000
Sahayatha Medical/Health 20.000000
-------------------------------------------------------------------------------------
Anupam industry wise investments
Industry
Food 13
Beauty/Fashion 11
Manufacturing 7
Medical/Health 6
Vehicles/Electrical Vehicles 3
Technology/Software 3
Education 2
Animal/Pets 1
Sports 1
Electronics 1
Liquor/Beverages 1
Services 1
Furnishing/Household 1
Name: count, dtype: int64
print("Total investments by Vineeta", shark_tank[shark_tank['Vineeta Investment Amount']>0][['Vineeta Investment Amount']].count().to_string()[-2:])
print("Investment amount by Vineeta", round(shark_tank['Vineeta Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Vineeta", round(shark_tank['Vineeta Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Vineeta", round(shark_tank['Vineeta Debt Amount'].sum()/100, 2), "crores\n")
print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Vineeta Investment Amount']>0][["Startup Name","Industry","Vineeta Investment Amount"]].to_string(index=False))
print('-'*75)
print("\nVineeta industry wise investments\n")
print(shark_tank[shark_tank['Vineeta Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Vineeta Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Vineeta Investment Amount']>0] [["Startup Name","Vineeta Investment Amount","Vineeta Investment Equity"]].sort_values(by="Vineeta Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Vineeta Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Vineeta 43
Investment amount by Vineeta 11.71 crores
Equity received by Vineeta 242.3 % in different companies
Debt/loan amount by Vineeta 2.29 crores
Company details:
---------------------------------------------------------------------------
Startup Name Industry Vineeta Investment Amount
BluePineFoods Food 25.00
BoozScooters Vehicles/Electrical Vehicles 20.00
HeartUpMySleeves Beauty/Fashion 12.50
NOCD Food 20.00
CosIQ Beauty/Fashion 25.00
JhaJiAchaar Food 28.30
SkippiIcePops Food 20.00
Get-A-Whey Food 33.33
TheQuirkyNaari Beauty/Fashion 17.50
SunfoxTechnologies Medical/Health 20.00
HumpyA2 Food 33.33
GoldSafeSolutions Manufacturing 16.66
WakaoFoods Food 25.00
KabaddiAdda Sports 40.00
NomadFoodProject Food 10.00
JainShikanji Food 10.00
SneaKare Beauty/Fashion 7.00
Dorji Food 10.00
WatchoutWearables Electronics 50.00
SoupX Food 50.00
Winston Beauty/Fashion 50.00
TeaFit Liquor/Beverages 12.50
TheSimplySalad Food 15.00
Paradyes Beauty/Fashion 32.50
Snitch Beauty/Fashion 30.00
HoneyVeda Food 25.00
PadCare Manufacturing 25.00
SwadeshiBlessings Furnishing/Household 12.50
OLL Technology/Software 15.00
Geeani Vehicles/Electrical Vehicles 33.33
TheGreenSnack Food 100.00
Mahantam Manufacturing 6.00
MindPeers Medical/Health 17.66
Freakins Beauty/Fashion 50.00
Perfora Furnishing/Household 26.66
Trunome Medical/Health 37.50
What'sUpWellness Food 20.00
HealthyMaster Food 50.00
NutriCook Food 50.00
ThePlatedProject Services 25.00
Rubans Beauty/Fashion 33.33
ForeverModest Beauty/Fashion 5.00
Naara-Aaba Liquor/Beverages 25.00
---------------------------------------------------------------------------
Vineeta industry wise investments
Industry
Food 17
Beauty/Fashion 10
Medical/Health 3
Manufacturing 3
Vehicles/Electrical Vehicles 2
Liquor/Beverages 2
Furnishing/Household 2
Sports 1
Electronics 1
Technology/Software 1
Services 1
Name: count, dtype: int64
print("Total investments by Aman", shark_tank[shark_tank['Aman Investment Amount']>=0][['Aman Investment Amount']].count().to_string()[-2:])
print("Investment amount by Aman", round(shark_tank['Aman Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Aman", round(shark_tank['Aman Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Aman", round(shark_tank['Aman Debt Amount'].sum()/100, 2), "crores\n")
print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Aman Investment Amount']>=0][["Startup Name","Industry","Aman Investment Amount"]].to_string(index=False))
print('-'*75)
print("\nAman industry wise investments\n")
print(shark_tank[shark_tank['Aman Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Aman Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Aman Investment Amount']>=0] [["Startup Name","Aman Investment Amount","Aman Investment Equity"]].sort_values(by="Aman Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Aman Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Aman 73
Investment amount by Aman 25.1 crores
Equity received by Aman 260.93 % in different companies
Debt/loan amount by Aman 3.11 crores
Company details:
---------------------------------------------------------------------------
Startup Name Industry Aman Investment Amount
BluePineFoods Food 25.000000
Peeschute Beauty/Fashion 75.000000
Bummer Beauty/Fashion 37.500000
RevampMoto Vehicles/Electrical Vehicles 50.000000
SkippiIcePops Food 20.000000
RaisingSuperstars Education 50.000000
Kavach Education 2.500000
BeyondSnack Food 25.000000
Altor Manufacturing 25.000000
Ariro Manufacturing 25.000000
Nuutjob Beauty/Fashion 8.330000
Meatyour Food 10.000000
EventBeep Education 10.000000
Farda Beauty/Fashion 15.000000
LOKA Technology/Software 13.330000
TheYarnBazaar Manufacturing 25.000000
TheRenalProject Medical/Health 50.000000
HammerLifestyle Electronics 100.000000
Cocofit Food 0.000016
BeyondWater Food 37.500000
Let'sTry Food 22.500000
FindYourKicksIndia Beauty/Fashion 10.000000
WeSTOCK Animal/Pets 15.000000
INACAN Food 20.000000
Get-A-Whey Food 33.330000
NamhyaFoods Food 50.000000
AyuRythm Medical/Health 75.000000
GrowFitter Technology/Software 50.000000
JainShikanji Food 10.000000
SneaKare Beauty/Fashion 7.000000
Hoovu Services 50.000000
VeryMuchIndian Beauty/Fashion 25.000000
Stage Entertainment 50.000000
GearHeadMotors Vehicles/Electrical Vehicles 50.000000
TeaFit Liquor/Beverages 12.500000
Haqdarshak Services 33.330000
TheSimplySalad Food 15.000000
AtypicalAdvantage Technology/Software 15.000000
HouseOfChikankari Beauty/Fashion 37.500000
Paradyes Beauty/Fashion 32.500000
Primebook Technology/Software 37.500000
GharSoaps Beauty/Fashion 60.000000
InsideFPV Manufacturing 18.750000
FastBeetle Services 45.000000
Bullspree Technology/Software 37.500000
Snitch Beauty/Fashion 30.000000
Portl Services 50.000000
Dabble Manufacturing 15.000000
Broomees Services 33.330000
Geeani Vehicles/Electrical Vehicles 33.330000
Manetain Beauty/Fashion 75.000000
UnStop Technology/Software 50.000000
BlueTea Food 50.000000
Zoff Food 100.000000
Mahantam Manufacturing 6.000000
MindPeers Medical/Health 17.660000
Daryaganj Food 90.000000
DhruvVidyut Vehicles/Electrical Vehicles 0.000000
TheHealthyBinge Food 25.000000
MeduLance Medical/Health 66.660000
neuphony Medical/Health 50.000000
Malaki Liquor/Beverages 25.000000
nawgati Services 33.500000
GladFul Food 16.660000
Pharmallama Medical/Health 40.000000
Hood Technology/Software 30.000000
Trunome Medical/Health 37.500000
Wol3D Manufacturing 80.000000
What'sUpWellness Food 20.000000
ThePlatedProject Services 25.000000
Rubans Beauty/Fashion 33.330000
Sahayatha Medical/Health 20.000000
TAC Beauty/Fashion 40.500000
---------------------------------------------------------------------------
Aman industry wise investments
Industry
Food 18
Beauty/Fashion 14
Medical/Health 8
Manufacturing 7
Technology/Software 7
Services 7
Vehicles/Electrical Vehicles 4
Education 3
Liquor/Beverages 2
Electronics 1
Animal/Pets 1
Entertainment 1
Name: count, dtype: int64
print("Total investments by Peyush", shark_tank[shark_tank['Peyush Investment Amount']>=0][['Peyush Investment Amount']].count().to_string()[-2:])
print("Investment amount by Peyush", round(shark_tank['Peyush Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Peyush", round(shark_tank['Peyush Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Peyush", round(shark_tank['Peyush Debt Amount'].sum()/100, 2), "crores\n")
print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Peyush Investment Amount']>=0][["Startup Name","Industry","Peyush Investment Amount"]].to_string(index=False))
print('-'*75)
print("\nPeyush industry wise investments\n")
print(shark_tank[shark_tank['Peyush Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Peyush Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Peyush Investment Amount']>=0] [["Startup Name","Peyush Investment Amount","Peyush Investment Equity"]].sort_values(by="Peyush Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Peyush Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Peyush 69
Investment amount by Peyush 22.1 crores
Equity received by Peyush 422.11 % in different companies
Debt/loan amount by Peyush 3.42 crores
Company details:
---------------------------------------------------------------------------
Startup Name Industry Peyush Investment Amount
VivalyfInnovations Medical/Health 28.000000
Ariro Manufacturing 25.000000
Nuutjob Beauty/Fashion 8.330000
Meatyour Food 10.000000
EventBeep Education 10.000000
LOKA Technology/Software 13.330000
Annie Education 35.000000
Carragreen Manufacturing 25.000000
TheYarnBazaar Manufacturing 25.000000
PNT Technology/Software 25.000000
FindYourKicksIndia Beauty/Fashion 10.000000
AasVidyalaya Education 50.000000
RoadBounce Technology/Software 80.000000
WeSTOCK Animal/Pets 15.000000
TheStatePlate Food 40.000000
INACAN Food 20.000000
Sid07Designs Hardware 25.000000
HairOriginals Beauty/Fashion 20.000000
KGAgrotech Agriculture 10.000000
SunfoxTechnologies Medical/Health 20.000000
IsakFragrances Beauty/Fashion 50.000000
WattTechnovations Medical/Health 0.000253
InsuranceSamadhan Services 100.000000
HumpyA2 Food 33.330000
GoldSafeSolutions Manufacturing 16.660000
TweekLabs Sports 20.000000
Proxgy Technology/Software 50.000000
StoreMyGoods Services 25.000000
WitBlox Manufacturing 30.000000
Hoovu Services 50.000000
Dorji Food 10.000000
Stage Entertainment 50.000000
GearHeadMotors Vehicles/Electrical Vehicles 50.000000
PatilKaki Food 20.000000
TeaFit Liquor/Beverages 12.500000
Haqdarshak Services 33.330000
HouseOfChikankari Beauty/Fashion 37.500000
ABCSports&Fitness Sports 40.000000
Primebook Technology/Software 37.500000
InsideFPV Manufacturing 18.750000
Kyari Manufacturing 25.500000
FastBeetle Services 45.000000
Sepal Manufacturing 50.000000
Solinas Services 45.000000
ekatra Furnishing/Household 10.000000
NeoMotion Manufacturing 100.000000
Bullspree Technology/Software 37.500000
Snitch Beauty/Fashion 30.000000
Portl Services 50.000000
Broomees Services 33.330000
PadCare Manufacturing 25.000000
OLL Technology/Software 15.000000
Mahantam Manufacturing 6.000000
MindPeers Medical/Health 53.000000
DhruvVidyut Vehicles/Electrical Vehicles 0.000000
iMumz Medical/Health 10.000000
TheHealthyBinge Food 25.000000
Perfora Furnishing/Household 26.660000
CureSee Medical/Health 50.000000
MeduLance Medical/Health 66.660000
neuphony Medical/Health 50.000000
Malaki Liquor/Beverages 25.000000
Pharmallama Medical/Health 40.000000
Hood Technology/Software 30.000000
GROWiT Agriculture 25.000000
Trunome Medical/Health 37.500000
SinghStyled Beauty/Fashion 50.000000
LilGoodness Food 50.000000
Sahayatha Medical/Health 20.000000
---------------------------------------------------------------------------
Peyush industry wise investments
Industry
Medical/Health 11
Manufacturing 11
Food 8
Technology/Software 8
Services 8
Beauty/Fashion 7
Education 3
Agriculture 2
Sports 2
Vehicles/Electrical Vehicles 2
Liquor/Beverages 2
Furnishing/Household 2
Animal/Pets 1
Hardware 1
Entertainment 1
Name: count, dtype: int64
print("Total investments by Amit", shark_tank[shark_tank['Amit Investment Amount']>=0][['Amit Investment Amount']].count().to_string()[-2:])
print("Investment amount by Amit", round(shark_tank['Amit Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Amit", round(shark_tank['Amit Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Amit", round(shark_tank['Amit Debt Amount'].sum()/100, 2), "crores\n")
print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Amit Investment Amount']>0][["Startup Name","Industry","Amit Investment Amount"]].to_string(index=False))
print('-'*75)
print("\nAmit industry wise investments\n")
print(shark_tank[shark_tank['Amit Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Amit Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Amit Investment Amount']>0] [["Startup Name","Amit Investment Amount","Amit Investment Equity"]].sort_values(by="Amit Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Amit Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Amit 21
Investment amount by Amit 8.01 crores
Equity received by Amit 109.0 % in different companies
Debt/loan amount by Amit 1.45 crores
Company details:
---------------------------------------------------------------------------
Startup Name Industry Amit Investment Amount
InsideFPV Manufacturing 18.75
Angrakhaa Beauty/Fashion 40.00
MoppFoods Food 75.00
Dobiee Food 72.00
Pflow Medical/Health 30.00
ekatra Furnishing/Household 10.00
licksters Food 25.00
ScrapUncle Services 60.00
UnStop Technology/Software 50.00
Cakelicious Food 25.00
Hornback Vehicles/Electrical Vehicles 50.00
nawgati Services 33.50
GladFul Food 16.66
Pharmallama Medical/Health 40.00
funngro Technology/Software 25.00
Aadvik Food 15.00
ForeverModest Beauty/Fashion 5.00
Sahayatha Medical/Health 20.00
maisha Beauty/Fashion 10.00
NishHair Beauty/Fashion 100.00
StyloBug Beauty/Fashion 80.00
---------------------------------------------------------------------------
Amit industry wise investments
Industry
Food 6
Beauty/Fashion 5
Medical/Health 3
Services 2
Technology/Software 2
Manufacturing 1
Furnishing/Household 1
Vehicles/Electrical Vehicles 1
Name: count, dtype: int64
print("Total investments by all Guests", shark_tank[shark_tank['Guest Investment Amount']>=0][['Guest Investment Amount']].count().to_string()[-2:])
print("Investment amount by all Guests", round(shark_tank['Guest Investment Amount'].sum()/100, 2), "crores")
print("Equity received by all Guests", round(shark_tank['Guest Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by all Guests", round(shark_tank['Guest Debt Amount'].sum()/100, 2), "crores\n")
print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Guest Investment Amount']>0][["Startup Name","Industry","Guest Investment Amount"]].to_string(index=False))
print('-'*75)
print("\nAll Guests industry wise investments\n")
print(shark_tank[shark_tank['Guest Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Guest Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Guest Investment Amount']>0] [["Startup Name","Guest Investment Amount","Guest Investment Equity"]].sort_values(by="Guest Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Guest Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by all Guests 37
Investment amount by all Guests 14.22 crores
Equity received by all Guests 123.7 % in different companies
Debt/loan amount by all Guests 1.95 crores
Company details:
---------------------------------------------------------------------------
Startup Name Industry Guest Investment Amount
TheSassBar Beauty/Fashion 25.000000
SunfoxTechnologies Medical/Health 20.000000
WattTechnovations Medical/Health 0.000253
HumpyA2 Food 33.330000
GoldSafeSolutions Manufacturing 16.660000
WakaoFoods Food 25.000000
NomadFoodProject Food 10.000000
WitBlox Manufacturing 30.000000
TAC Beauty/Fashion 40.500000
Naara-Aaba Liquor/Beverages 25.000000
RodBez Vehicles/Electrical Vehicles 10.000000
Blix Technology/Software 40.000000
TURMS Beauty/Fashion 120.000000
mintree Beauty/Fashion 45.000000
DilFoods Food 100.000000
GoenchiFeni Liquor/Beverages 200.000000
GudGum Food 20.000000
EvaScalp Medical/Health 10.000000
HoneyTwigs Food 25.000000
JewelBox Beauty/Fashion 80.000000
DaakRoom Services 36.000000
NasherMiles Beauty/Fashion 60.000000
Without Beauty/Fashion 37.500000
Kibo Technology/Software 30.000000
YesMadam Beauty/Fashion 37.500000
ToffeeCoffeeRoasters Liquor/Beverages 35.000000
Chefling Food 10.000000
ToHands Technology/Software 60.000000
PlusGold Technology/Software 60.000000
Aroleap Medical/Health 25.000000
WiseLife Medical/Health 30.000000
ModelVerse Technology/Software 8.330000
MEPACK Services 3.500000
Deeva Beauty/Fashion 50.000000
Sama Services 33.330000
Dharaksha Manufacturing 0.002500
iDreamCareer Technology/Software 30.000000
---------------------------------------------------------------------------
All Guests industry wise investments
Industry
Beauty/Fashion 9
Food 7
Technology/Software 6
Medical/Health 5
Manufacturing 3
Liquor/Beverages 3
Services 3
Vehicles/Electrical Vehicles 1
Name: count, dtype: int64
# Guest sharks and number of companies they invested
shark_tank.loc[shark_tank['Guest Investment Amount'] > 1]['Invested Guest Name'].str.split(',').explode('Guest Name').value_counts().sort_values(ascending=False)
Invested Guest Name Ritesh Aggarwal 17 Ghazal Alagh 7 Azhar Iqubal 4 Radhika Gupta 4 Vikas D Nahar 2 Ronnie Screwvala 2 Varun Dua 2 Deepinder Goyal 1 Name: count, dtype: int64
# Investment amount by guests, in lakhs
round(shark_tank.groupby(["Invested Guest Name"])["Guest Investment Amount"].sum().sort_values(ascending=False))
Invested Guest Name Ritesh Aggarwal 379.0 Ritesh Aggarwal,Radhika Gupta 230.0 Azhar Iqubal 200.0 Deepinder Goyal 200.0 Ghazal Alagh 160.0 Ronnie Screwvala 68.0 Vikas D Nahar 66.0 Varun Dua 60.0 Varun Dua,Radhika Gupta 60.0 Name: Guest Investment Amount, dtype: float64
# tmpdf = shark_tank.loc[shark_tank['Guest Investment Amount'] > 1]
# tmpdf[['Invested Guest Name','Guest Investment Amount']]
# Number of sharks in a deal, in all seasons
print(shark_tank['Number of Sharks in Deal'].value_counts(), "\n")
# In percentage
print(round(shark_tank['Number of Sharks in Deal'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False))
fig = plt.figure(figsize=(8, 6))
plt.title("Number of sharks in a deal, in all seasons", fontsize=15)
plt.xticks(fontsize=15)
plt.yticks([])
ax = sns.countplot(data = shark_tank, x = 'Number of Sharks in Deal')
ax.set_ylabel('')
for t in ax.patches:
if (np.isnan(float(t.get_height()))):
ax.annotate(0, (t.get_x(), 0))
else:
ax.annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01), size=14)
Number of Sharks in Deal 1.0 107 2.0 75 3.0 36 4.0 18 5.0 12 Name: count, dtype: int64 Number of Sharks in Deal 1.0 43% 2.0 30% 3.0 15% 4.0 7% 5.0 5% Name: proportion, dtype: object
# All sharks deals
print(shark_tank.loc[shark_tank['Number of Sharks in Deal'] >= 5][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity"]])
Season Number Startup Name Total Deal Amount Total Deal Equity 15 1 SkippiIcePops 100.0000 15.0 50 1 FindYourKicksIndia 50.0000 25.0 64 1 INACAN 100.0000 10.0 80 1 SunfoxTechnologies 100.0000 6.0 209 2 Snitch 150.0000 1.5 239 2 Mahantam 30.0000 20.0 274 2 Pharmallama 200.0000 5.0 311 2 Sahayatha 100.0000 10.0 357 3 JewelBox 200.0000 6.0 365 3 NasherMiles 300.0000 1.5 423 3 LittleBox 75.0000 2.5 435 3 Dharaksha 0.0125 1.0
# Sharks with most number of solo deals
amt_cols = shark_tank.columns[shark_tank.columns.str.contains(' Investment Amount')].tolist()
tmp = shark_tank.loc[shark_tank['Number of Sharks in Deal'] == 1][amt_cols]
tmp.count().sort_values(ascending=False).nlargest(3)
# Namita did most number of solo deals, than any other Shark
Namita Investment Amount 24 Aman Investment Amount 21 Peyush Investment Amount 17 dtype: int64
# Sharks with most number of episode presence, in all seasons
present_cols = shark_tank.columns[shark_tank.columns.str.endswith(' Present')].tolist()
tmp = shark_tank[present_cols]
tmp.sum().sort_values(ascending=False).nlargest(3)
# Anupam was there in most number of episodes, in 3 seasons
Anupam Present 390.0 Aman Present 383.0 Namita Present 361.0 dtype: float64
# Sharks with most number of episode presence, in current/latest season (3rd Season)
tmp = shark_tank.loc[shark_tank['Season Number'] == 3][present_cols]
tmp.sum().sort_values(ascending=False).nlargest(4)
Aman Present 111.0 Anupam Present 108.0 Guest Present 102.0 Vineeta Present 96.0 dtype: float64
# Anchor and number of pitches, they hosted
shark_tank.groupby('Anchor').size()
Anchor Rahul Dua 289 Rannvijay Singh 152 dtype: int64
# Anchor and number of episodes, they hosted
pd.pivot_table(shark_tank, values='Episode Number', columns='Anchor', aggfunc='max')
| Anchor | Rahul Dua | Rannvijay Singh |
|---|---|---|
| Episode Number | 51 | 36 |
# Sharks
# tmp = shark_tank.loc[shark_tank['Number of Sharks in Deal'] == 2][amt_cols].stack()
# tmp
# tmp2 = shark_tank.loc[shark_tank['Number of Sharks in Deal'] == 2][amt_cols].transpose()
# tmp2
print(shark_tank['Pitchers State'].str.split(',').explode('Pitchers State').value_counts(), "\n")
shark_tank['Pitchers State'].str.split(',').explode('Pitchers State').value_counts().sort_values().plot.barh()
Pitchers State Maharashtra 127 Delhi 67 Karnataka 52 Gujarat 46 Haryana 30 Uttar Pradesh 24 West Bengal 19 Rajasthan 17 Telangana 16 Punjab 12 Tamil Nadu 9 Madhya Pradesh 8 Bihar 5 Goa 4 Jammu & Kashmir 4 Kerala 4 Uttarakhand 3 Jharkhand 3 Himachal Pradesh 2 Chhattisgarh 2 Assam 2 Andhra Pradesh 1 Arunachal Pradesh 1 Tamilnadu 1 Bihar 1 Kerala 1 Name: count, dtype: int64
<Axes: ylabel='Pitchers State'>
# Top 20 Indian Cities
tmp = shark_tank['Pitchers City'].value_counts().nlargest(20).sort_values(ascending=True)
fig = px.bar(tmp, x=tmp.values, title="<b>Indian top 20 cities</b> with number of startups came for pitching", template='simple_white', text=tmp, width=850, height=800)
fig.update_yaxes(title_text="")
fig.update_xaxes(visible=False)
fig.show()
# Most frequently asked amount, by startups
shark_tank.groupby('Original Ask Amount').size().nlargest(10)
# Original Ask Amount (in lakhs) and Number of times asked
Original Ask Amount 50.0 94 100.0 77 75.0 52 60.0 27 40.0 22 80.0 22 150.0 20 30.0 19 90.0 11 200.0 11 dtype: int64
# Most frequently offered equity, by startups
shark_tank.groupby('Original Offered Equity').size().nlargest(10)
# Original Offered Equity (in %) and Number of times offered
Original Offered Equity 1.0 89 2.0 67 5.0 67 10.0 40 3.0 37 4.0 26 2.5 25 0.5 17 1.5 15 7.5 11 dtype: int64
# ✅ Most frequently invested amount, by Sharks
shark_tank.groupby('Total Deal Amount').size().nlargest(10)
# Sharks mostly invested between 50K-1lakh per deal
# Total Deal Amount (in lakhs) and Number of times invested
Total Deal Amount 50.0 47 100.0 39 75.0 24 60.0 20 40.0 14 30.0 13 25.0 12 20.0 9 80.0 9 10.0 7 dtype: int64
# shark_tank.groupby(['Original Ask Amount','Received Offer']).size().nlargest(100)
# ✅ Most frequently received total equity, by Sharks
shark_tank.groupby('Total Deal Equity').size().nlargest(10)
# Sharks are expecting around 10-20% equity, in a deal
# Total Deal Equity (in %) and Number of times invested
Total Deal Equity 10.0 30 1.0 23 5.0 23 2.0 17 4.0 17 3.0 16 20.0 16 15.0 14 6.0 13 2.5 9 dtype: int64
# ✅ Mostly successful combinations (of asked amount and offered equity)
shark_tank.loc[shark_tank['Received Offer'] == 1].groupby(['Original Ask Amount','Original Offered Equity']).size().nlargest(10)
Original Ask Amount Original Offered Equity
50.0 5.0 16
100.0 1.0 15
50.0 2.0 10
100.0 2.0 10
60.0 2.0 8
50.0 1.0 7
3.0 7
75.0 4.0 7
50.0 10.0 6
70.0 1.0 6
dtype: int64
# Most frequently asked amount, by startups who could NOT get a deal
shark_tank.loc[shark_tank['Received Offer'] == 0].groupby('Original Ask Amount').size().nlargest(10)
# Original Ask Amount (in lakhs) and Number of times asked (but rejected by sharks) ❌
Original Ask Amount 50.0 30 100.0 23 75.0 16 60.0 9 80.0 8 40.0 6 90.0 6 200.0 6 20.0 4 25.0 4 dtype: int64
# Most frequently offered equity, by startups who could NOT get a deal
shark_tank.loc[shark_tank['Received Offer'] == 0].groupby('Original Offered Equity').size().nlargest(10)
# Original Offered Equity (in %) and Number of times offered (but rejected by sharks) ❌
Original Offered Equity 5.0 29 1.0 24 2.0 17 10.0 14 3.0 12 2.5 11 1.5 4 4.0 4 7.5 4 15.0 4 dtype: int64
# Mostly rejected combinations (of asked amount and offered equity)
shark_tank.loc[shark_tank['Received Offer'] == 0].groupby(['Original Ask Amount','Original Offered Equity']).size().nlargest(5)
# You may not get deal, if you ask for 1 crore with 1% equity or 50K with 5%/10% equity 🔴
Original Ask Amount Original Offered Equity
100.0 1.0 9
50.0 5.0 8
10.0 5
75.0 5.0 5
60.0 2.0 4
dtype: int64
shp_gdf = gpd.read_file('../input/india-gis-data/India States/Indian_states.shp')
merged = shp_gdf.set_index('st_nm').join(shark_tank.set_index('Pitchers State'))
merged['Total Deal Amount'] = merged['Total Deal Amount'].fillna(0)
fig, ax = plt.subplots(1, figsize=(12, 12))
ax.axis('off')
ax.set_title('As per Total Deal Amount', fontdict={'fontsize': '15', 'fontweight' : '3'})
fig = merged.plot(column='Total Deal Amount', cmap='YlGn', linewidth=0.8, ax=ax, edgecolor='0.5', legend=True)
# All season's, startup companies incorporated year
fig = plt.figure(figsize=(10, 7))
plt.title('Number of companies and Year of establishment of startups', size=14)
tmp = shark_tank.loc[shark_tank['Started in'].notnull()]
ax = sns.countplot(data = tmp, x = 'Started in')
ax.set_xlabel('Started in', fontsize=13)
plt.yticks([])
ax.set_ylabel('')
for t in ax.patches:
if (np.isnan(float(t.get_height()))):
ax.annotate(0, (t.get_x(), 0))
else:
ax.annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.02), size=14)
# Some companies got more amount than they asked/expected
print(shark_tank.loc[shark_tank['Original Ask Amount'] < shark_tank["Total Deal Amount"]][["Startup Name"]].count())
shark_tank.loc[shark_tank['Original Ask Amount'] < shark_tank["Total Deal Amount"]][["Season Number","Startup Name","Original Ask Amount","Total Deal Amount"]]
Startup Name 35 dtype: int64
| Season Number | Startup Name | Original Ask Amount | Total Deal Amount | |
|---|---|---|---|---|
| 0 | 1 | BluePineFoods | 50.0 | 75.0 |
| 10 | 1 | JhaJiAchaar | 50.0 | 56.6 |
| 15 | 1 | SkippiIcePops | 45.0 | 100.0 |
| 37 | 1 | Annie | 30.0 | 105.0 |
| 39 | 1 | TheYarnBazaar | 50.0 | 100.0 |
| 43 | 1 | HammerLifestyle | 30.0 | 100.0 |
| 59 | 1 | WeSTOCK | 50.0 | 60.0 |
| 64 | 1 | INACAN | 50.0 | 100.0 |
| 76 | 1 | TheSassBar | 40.0 | 50.0 |
| 89 | 1 | HumpyA2 | 75.0 | 100.0 |
| 109 | 1 | TweekLabs | 40.0 | 60.0 |
| 110 | 1 | Proxgy | 35.0 | 100.0 |
| 118 | 1 | SneaKare | 20.0 | 21.0 |
| 152 | 2 | Hoovu | 80.0 | 100.0 |
| 161 | 2 | GearHeadMotors | 75.0 | 100.0 |
| 178 | 2 | Zillionaire | 50.0 | 100.0 |
| 216 | 2 | Broomees | 80.0 | 100.0 |
| 219 | 2 | PadCare | 50.0 | 100.0 |
| 223 | 2 | Geeani | 75.0 | 100.0 |
| 230 | 2 | UnStop | 100.0 | 200.0 |
| 240 | 2 | MindPeers | 53.0 | 106.0 |
| 257 | 2 | CureSee | 40.0 | 50.0 |
| 274 | 2 | Pharmallama | 100.0 | 200.0 |
| 283 | 2 | What'sUpWellness | 50.0 | 60.0 |
| 333 | 3 | DilFoods | 50.0 | 200.0 |
| 334 | 3 | AIKavach/Panoplia | 50.0 | 100.0 |
| 337 | 3 | Kalakaram | 50.0 | 60.0 |
| 343 | 3 | WYLDCard | 50.0 | 75.0 |
| 348 | 3 | GoenchiFeni | 100.0 | 200.0 |
| 351 | 3 | GudGum | 50.0 | 80.0 |
| 357 | 3 | JewelBox | 100.0 | 200.0 |
| 363 | 3 | ALittleExtra | 48.0 | 60.0 |
| 372 | 3 | HyperLab | 10.0 | 25.0 |
| 394 | 3 | ToHands | 55.0 | 60.0 |
| 405 | 3 | WiseLife | 60.0 | 120.0 |
# Most of the companies diluted/gave their company equity more than they initially offered/expected
shark_tank.loc[shark_tank['Original Offered Equity'] < shark_tank["Total Deal Equity"]][["Season Number","Startup Name","Original Offered Equity","Total Deal Equity"]]
| Season Number | Startup Name | Original Offered Equity | Total Deal Equity | |
|---|---|---|---|---|
| 0 | 1 | BluePineFoods | 5.0 | 16.00 |
| 1 | 1 | BoozScooters | 15.0 | 50.00 |
| 2 | 1 | HeartUpMySleeves | 10.0 | 30.00 |
| 3 | 1 | TagzFoods | 1.0 | 2.75 |
| 7 | 1 | Peeschute | 4.0 | 6.00 |
| ... | ... | ... | ... | ... |
| 424 | 3 | CremeCastle | 1.5 | 2.50 |
| 427 | 3 | Deeva | 4.0 | 6.00 |
| 428 | 3 | DesignTemplate | 2.5 | 10.00 |
| 429 | 3 | Sama | 1.0 | 1.50 |
| 440 | 3 | Smotect | 1.0 | 5.00 |
199 rows × 4 columns
# Below (19) companies got the same valuation they requested (with or without loan)
print(shark_tank.loc[shark_tank['Valuation Requested'] == shark_tank["Deal Valuation"]][["Startup Name"]].count())
shark_tank.loc[shark_tank['Valuation Requested'] == shark_tank["Deal Valuation"]][["Season Number","Startup Name","Valuation Requested","Deal Valuation"]]
Startup Name 28 dtype: int64
| Season Number | Startup Name | Valuation Requested | Deal Valuation | |
|---|---|---|---|---|
| 20 | 1 | Kavach | 50.00 | 50.00 |
| 22 | 1 | BeyondSnack | 2000.00 | 2000.00 |
| 45 | 1 | Cocofit | 0.00 | 0.00 |
| 86 | 1 | WattTechnovations | 0.00 | 0.00 |
| 171 | 2 | TheSimplySalad | 300.00 | 300.00 |
| 185 | 2 | Janitri | 4000.00 | 4000.00 |
| 203 | 2 | NeoMotion | 10000.00 | 10000.00 |
| 219 | 2 | PadCare | 2500.00 | 2500.00 |
| 223 | 2 | Geeani | 1000.00 | 1000.00 |
| 240 | 2 | MindPeers | 5300.00 | 5300.00 |
| 243 | 2 | DhruvVidyut | 0.00 | 0.00 |
| 253 | 2 | TheHealthyBinge | 1000.00 | 1000.00 |
| 311 | 2 | Sahayatha | 1000.00 | 1000.00 |
| 314 | 2 | NishHair | 5000.00 | 5000.00 |
| 334 | 3 | AIKavach/Panoplia | 4000.00 | 4000.00 |
| 341 | 3 | WeHear | 25000.00 | 25000.00 |
| 354 | 3 | HoneyTwigs | 2500.00 | 2500.00 |
| 355 | 3 | Koparo | 7000.00 | 7000.00 |
| 363 | 3 | ALittleExtra | 800.00 | 800.00 |
| 390 | 3 | Matri | 1500.00 | 1500.00 |
| 397 | 3 | Cosmix | 10000.00 | 10000.00 |
| 399 | 3 | PolishMePretty | 100.00 | 100.00 |
| 405 | 3 | WiseLife | 3000.00 | 3000.00 |
| 408 | 3 | AvataarSkincare | 7000.00 | 7000.00 |
| 412 | 3 | ModelVerse | 250.00 | 250.00 |
| 414 | 3 | TheShellHair | 1000.00 | 1000.00 |
| 416 | 3 | MEPACK | 70.00 | 70.00 |
| 435 | 3 | Dharaksha | 1.25 | 1.25 |
# There is 1 company which got more valuation than they pitched, JhaJi Achaar received after the Season (in 2023)
shark_tank.loc[shark_tank['Valuation Requested'] < shark_tank["Deal Valuation"]][["Startup Name","Valuation Requested","Deal Valuation"]]
| Startup Name | Valuation Requested | Deal Valuation | |
|---|---|---|---|
| 10 | JhaJiAchaar | 500.0 | 1007.0 |
| 372 | HyperLab | 1000.0 | 2500.0 |
# Some companies were on pre-revenue or didn't had any revenue (as of pitching day)
shark_tank.loc[shark_tank['Yearly Revenue'] == 0]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | 1 | CosIQ | 4 | 10 | 20-Dec-21 | 4-Feb-22 | 23-Dec-21 | Entrepreneurship Ki Wave | Rannvijay Singh | Beauty/Fashion | Intelligent Skincare | https://mycosiq.com/ | 2021 | 2 | 1 | 1 | <NA> | 1 | Middle | Delhi | Delhi | 0 | 4.0 | 75 | 20 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
| 23 | 1 | VivalyfInnovations | 8 | 24 | 20-Dec-21 | 4-Feb-22 | 29-Dec-21 | Shark Ko Impress Karne Wale Ideas | Rannvijay Singh | Medical/Health | Easy Life Prickless Diabetes Testing Machine | https://vivalyf.in/ | 2021 | 2 | 1 | 1 | <NA> | 0 | Young | Hyderabad | Telangana | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | 28.0 | 16.66 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN |
| 24 | 1 | MotionBreeze | 8 | 25 | 20-Dec-21 | 4-Feb-22 | 29-Dec-21 | Shark Ko Impress Karne Wale Ideas | Rannvijay Singh | Vehicles/Electrical Vehicles | Smart Electric Motorcycle | https://www.motionautomotive.in/ | 2018 | 4 | 4 | <NA> | <NA> | 0 | Middle | Vadodara | Gujarat | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 30.0 | 6.0 | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN |
| 42 | 1 | GoodGoodPiggy | 14 | 43 | 20-Dec-21 | 4-Feb-22 | 6-Jan-22 | Naye Aur Nayab Pitchers | Rannvijay Singh | Technology/Software | Digital Piggy Bank | https://goodgoodpiggy.com/ | 2021 | 2 | <NA> | 2 | <NA> | 0 | Young | Delhi | Delhi | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN |
| 77 | 1 | KGAgrotech | 24 | 78 | 20-Dec-21 | 4-Feb-22 | 20-Jan-22 | A Decade Of Indian Entrepreneurship | Rannvijay Singh | Agriculture | Agricultural Innovations | https://www.instagram.com/jugaadu_kamlesh/ | 2022 | 2 | 2 | <NA> | <NA> | 0 | Young | Malegaon | Maharashtra | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | 10.0 | 40.00 | 20.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Ghazal Alagh | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | NaN | 1.0 |
| 83 | 1 | JulaaAutomation | 26 | 84 | 20-Dec-21 | 4-Feb-22 | 24-Jan-22 | Revolutionary Ideas | Rannvijay Singh | Manufacturing | Automatic Cradle | https://www.automaticjulaa.com/ | 2022 | 3 | 3 | <NA> | <NA> | 0 | Middle | Ahmedabad | Gujarat | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Ghazal Alagh | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | NaN | 1.0 |
| 98 | 1 | Scholify | 30 | 99 | 20-Dec-21 | 4-Feb-22 | 28-Jan-22 | Sharks Ki Expertise | Rannvijay Singh | Education | Scholarship Platform | https://scholifyme.com/ | 2018 | 1 | 1 | <NA> | <NA> | 0 | Middle | Bangalore | Karnataka | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
| 100 | 1 | Sabjikothi | 31 | 101 | 20-Dec-21 | 4-Feb-22 | 31-Jan-22 | Entrepreneurship Ki Raah | Rannvijay Singh | Manufacturing | Vegetables Storage SaptKrishi | https://www.saptkrishi.com/ | 2019 | 2 | 1 | 1 | <NA> | 0 | Young | Bhagalpur | Bihar | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 | NaN |
| 114 | 1 | On2Cook | 34 | 115 | 20-Dec-21 | 4-Feb-22 | 3-Feb-22 | Scaling Ambitions | Rannvijay Singh | Food | Fastest Cooking Device | https://on2cook.com/ | 2022 | 1 | 1 | <NA> | <NA> | 0 | Middle | Ahmedabad | Gujarat | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Ghazal Alagh | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | 1.0 | 1.0 |
| 131 | 1 | Scintiglo | 0 | 132 | 20-Dec-21 | 4-Feb-22 | NaN | Unseen | Rannvijay Singh | Medical/Health | Diagnostic device for microalbuminuria estimation | https://cemd.in/ | 2021 | 1 | 1 | <NA> | <NA> | 0 | Middle | Indore | Madhya Pradesh | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 205 | 2 | Sayonara | 18 | 206 | 2-Jan-23 | 10-Mar-23 | 25-Jan-23 | Business Ideas With Potential | Rahul Dua | Beauty/Fashion | Petticoat | NaN | <NA> | 1 | 1 | <NA> | <NA> | 0 | Middle | Kolkata | West Bengal | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN |
| 206 | 2 | PMV | 19 | 207 | 2-Jan-23 | 10-Mar-23 | 26-Jan-23 | Building Brands For India | Rahul Dua | Vehicles/Electrical Vehicles | Personal Mobility Vehicle | https://pmvelectric.com/ | 2018 | 1 | 1 | <NA> | <NA> | 0 | Middle | Mumbai | Maharashtra | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 257 | 2 | CureSee | 34 | 258 | 2-Jan-23 | 10-Mar-23 | 16-Feb-23 | Growing Ideas Into Successful Businesses | Rahul Dua | Medical/Health | Artificial Intelligence (AI) based vision therapy | https://curesee.com/ | 2019 | 3 | 3 | <NA> | <NA> | 0 | Middle | Gurgaon | Haryana | 0 | 264.0 | <NA> | <NA> | ... | NaN | NaN | NaN | 50.0 | 10.00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 264 | 2 | HoloKitab | 36 | 265 | 2-Jan-23 | 10-Mar-23 | 20-Feb-23 | Anokhe Pitchers Ke Anokhe Ideas | Rahul Dua | Technology/Software | Augmented Reality content for books | https://www.holokitab.in/ | <NA> | 2 | 2 | <NA> | <NA> | 0 | Middle | Jalandhar | Punjab | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 277 | 2 | Hood | 40 | 278 | 2-Jan-23 | 10-Mar-23 | 24-Feb-23 | Creating Valuable Businesses | Rahul Dua | Technology/Software | Pseudonymous social network | https://www.hood.live/ | 2022 | 3 | 3 | <NA> | <NA> | 0 | Middle | Gurgaon | Haryana | 0 | NaN | <NA> | <NA> | ... | 30.0 | 0.27 | 30.0 | 30.0 | 0.27 | 30.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN |
| 295 | 2 | WaggyZone | 44 | 296 | 2-Jan-23 | 10-Mar-23 | 2-Mar-23 | Entrepreneurship Ka Junoon | Rahul Dua | Animal/Pets | Ice Cream Treat for Dogs, Puppies and Cats | https://waggyzone.com/ | <NA> | 1 | <NA> | 1 | <NA> | 0 | Middle | Mumbai | Maharashtra | 0 | 1.0 | 60 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 350 | 3 | Vecros | 10 | 351 | 22-Jan-24 | NaN | 2-Feb-24 | Pitching Innovation | Rahul Dua | Technology/Software | Spatial AI drone | https://vecros.com/ | 2018 | 2 | 1 | 1 | <NA> | 0 | Young | Delhi | Delhi | 0 | 9.0 | <NA> | <NA> | ... | 20.0 | 1.00 | 80.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Deepinder Goyal | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN | 1.0 |
| 415 | 3 | Rize | 32 | 416 | 22-Jan-24 | NaN | 5-Mar-24 | Young Entrepreneurs Make Their Mark | Rahul Dua | Food | Energy Bars | https://rizebar.in/ | 2023 | 2 | 2 | <NA> | <NA> | 0 | Young | Gurgaon | Haryana | 0 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Ritesh Aggarwal | 1.0 | NaN | 1.0 | 1.0 | NaN | 1.0 | NaN | 1.0 |
18 rows × 78 columns
# Some companies were on burning/paying money from their pocket, without any profit (as of pitching day)
shark_tank.loc[shark_tank['Cash Burn'] == 'yes']
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 62 | 1 | TheStatePlate | 20 | 63 | 20-Dec-21 | 4-Feb-22 | 14-Jan-22 | A Variety Of Ideas | Rannvijay Singh | Food | Delicacies | https://thestateplate.com/ | 2020 | 2 | 1 | 1 | <NA> | 0 | Young | Bangalore,Kolkata | Karnataka,West Bengal | <NA> | 40.0 | 34 | <NA> | ... | NaN | NaN | NaN | 40.00 | 3.000 | 25.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN |
| 81 | 1 | Alpino | 25 | 82 | 20-Dec-21 | 4-Feb-22 | 21-Jan-22 | An Ocean Of Opportunities | Rannvijay Singh | Food | Roasted Peanut butter Products | https://alpino.store/ | 2016 | 4 | 4 | <NA> | <NA> | 0 | Young | Surat | Gujarat | <NA> | NaN | 38 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Ghazal Alagh | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | NaN | 1.0 |
| 105 | 1 | GrowFitter | 32 | 106 | 20-Dec-21 | 4-Feb-22 | 1-Feb-22 | The Road To Success | Rannvijay Singh | Technology/Software | Rewards App | https://www.growfitter.com/ | 2021 | 2 | 2 | <NA> | <NA> | 0 | Middle | Mumbai | Maharashtra | 170 | NaN | <NA> | <NA> | ... | 50.00 | 2.0000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN |
| 137 | 1 | ZyppElectric | 0 | 138 | 20-Dec-21 | 4-Feb-22 | NaN | Unseen | Rannvijay Singh | Vehicles/Electrical Vehicles | Electrical Vehicles | https://zypp.app/ | 2017 | 2 | 1 | 1 | <NA> | 1 | Young | Gurgaon | Haryana | <NA> | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 140 | 1 | HappyBar | 0 | 141 | 20-Dec-21 | 4-Feb-22 | NaN | Unseen | Rannvijay Singh | Food | FitSport delicious snacks | https://www.fitsport.me/ | 2019 | 3 | 2 | 1 | <NA> | 0 | Middle | Hyderabad | Telangana | <NA> | 29.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 157 | 2 | SoupX | 2 | 158 | 2-Jan-23 | 10-Mar-23 | 3-Jan-23 | A Bigger Vision | Rahul Dua | Food | Soup based meals | https://www.soupx.in/ | <NA> | 2 | 2 | <NA> | <NA> | 0 | Young | Delhi | Delhi | <NA> | NaN | 45 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 165 | 2 | Flatheads | 5 | 166 | 2-Jan-23 | 10-Mar-23 | 6-Jan-23 | Investing in the Future of India | Rahul Dua | Beauty/Fashion | Shoes Sneakers Loafers | https://www.flatheads.in/ | 2019 | 1 | 1 | <NA> | <NA> | 0 | Middle | Bangalore | Karnataka | <NA> | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 194 | 2 | FastBeetle | 15 | 195 | 2-Jan-23 | 10-Mar-23 | 20-Jan-23 | Changing The Face Of Indian Entrepreneurship | Rahul Dua | Services | Local courier and parcel service | https://www.fastbeetle.com/ | 2019 | 2 | 2 | <NA> | <NA> | 0 | Young | Srinagar | Jammu & Kashmir | <NA> | 25.0 | 54 | <NA> | ... | 45.00 | 3.7500 | NaN | 45.00 | 3.750 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN |
| 196 | 2 | VSMani | 16 | 197 | 2-Jan-23 | 10-Mar-23 | 23-Jan-23 | Pitchers Ki Taiyyari | Rahul Dua | Food | Coffee and snacks | https://vsmani.com/ | 2020 | 3 | 3 | <NA> | <NA> | 0 | Middle | Bangalore | Karnataka | <NA> | 63.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN |
| 208 | 2 | Bullspree | 19 | 209 | 2-Jan-23 | 10-Mar-23 | 26-Jan-23 | Building Brands For India | Rahul Dua | Technology/Software | App to learn stock market basics | https://bullspree.com/ | <NA> | 3 | 3 | <NA> | <NA> | 0 | Middle | Ahmedabad | Gujarat | <NA> | NaN | <NA> | <NA> | ... | 37.50 | 1.4300 | NaN | 37.50 | 1.430 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 214 | 2 | CloudTailor | 21 | 215 | 2-Jan-23 | 10-Mar-23 | 30-Jan-23 | Adhbhut Aur Anokhe Entrepreneurs | Rahul Dua | Services | Custom tailor online | https://www.cloudtailor.com/ | <NA> | 3 | 2 | 1 | <NA> | 1 | Middle | Bangalore,Hyderabad | Karnataka,Telangana | <NA> | 34.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 219 | 2 | PadCare | 23 | 220 | 2-Jan-23 | 10-Mar-23 | 1-Feb-23 | Changing The World | Rahul Dua | Manufacturing | Menstrual hygiene disposal solution | https://www.padcarelabs.com/ | <NA> | 1 | 1 | <NA> | <NA> | 0 | Young | Pune | Maharashtra | <NA> | 14.0 | <NA> | <NA> | ... | NaN | NaN | NaN | 25.00 | 1.000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 232 | 2 | TheGreenSnack | 27 | 233 | 2-Jan-23 | 10-Mar-23 | 7-Feb-23 | Nayi Soch Naye Vichaar | Rahul Dua | Food | Healthy Snacks Online | https://thegreensnackco.com/ | 2017 | 2 | 1 | 1 | <NA> | 1 | Middle | Mumbai | Maharashtra | <NA> | 25.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | NaN |
| 241 | 2 | Barosi | 29 | 242 | 2-Jan-23 | 10-Mar-23 | 9-Feb-23 | Pulse Of The Country | Rahul Dua | Food | Fresh & pure milk products | https://www.barosi.in/ | 2016 | 1 | 1 | <NA> | <NA> | 0 | Middle | Pataudi | Haryana | <NA> | 42.0 | 40 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 247 | 2 | Nirmalaya | 31 | 248 | 2-Jan-23 | 10-Mar-23 | 13-Feb-23 | Innovations And Investments | Rahul Dua | Manufacturing | Incense products made from temple flowers | https://nirmalaya.com/ | <NA> | 3 | 2 | 1 | <NA> | 1 | Middle | Delhi | Delhi | <NA> | 80.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 253 | 2 | TheHealthyBinge | 33 | 254 | 2-Jan-23 | 10-Mar-23 | 15-Feb-23 | Growing With India | Rahul Dua | Food | Assorted Pack Baked Chips | https://www.healthybinge.co.in/ | <NA> | 2 | 2 | <NA> | <NA> | 0 | Middle | Pune | Maharashtra | <NA> | 11.0 | <NA> | <NA> | ... | 25.00 | 2.5000 | NaN | 25.00 | 2.500 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 254 | 2 | Freakins | 33 | 255 | 2-Jan-23 | 10-Mar-23 | 15-Feb-23 | Growing With India | Rahul Dua | Beauty/Fashion | Fashionable Denim Apparel | https://freakins.com/ | <NA> | 2 | 2 | <NA> | <NA> | 0 | Middle | Mumbai | Maharashtra | <NA> | NaN | 63 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 255 | 2 | Perfora | 34 | 256 | 2-Jan-23 | 10-Mar-23 | 16-Feb-23 | Growing Ideas Into Successful Businesses | Rahul Dua | Furnishing/Household | Toothpaste Electric Toothbrush | https://perforacare.com/ | <NA> | 2 | 2 | <NA> | <NA> | 0 | Young | Ballarpur,Karnal | Haryana,Maharashtra | <NA> | NaN | 57 | <NA> | ... | NaN | NaN | NaN | 26.66 | 0.833 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 262 | 2 | neuphony | 36 | 263 | 2-Jan-23 | 10-Mar-23 | 20-Feb-23 | Anokhe Pitchers Ke Anokhe Ideas | Rahul Dua | Medical/Health | Wearable EEG Headband | https://neuphony.com/ | 2022 | 2 | 1 | 1 | <NA> | 1 | Young | Noida | Uttar Pradesh | <NA> | NaN | <NA> | <NA> | ... | 50.00 | 2.7000 | NaN | 50.00 | 2.700 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 273 | 2 | GladFul | 39 | 274 | 2-Jan-23 | 10-Mar-23 | 23-Feb-23 | Revolutionary Ideas And Successful Businesses | Rahul Dua | Food | Natural High Protein Rich & Healthy Foods | https://gladful.in/ | 2022 | 2 | 1 | 1 | <NA> | 0 | Middle | Jaipur | Rajasthan | <NA> | 24.0 | <NA> | <NA> | ... | 16.66 | 1.1666 | NaN | NaN | NaN | NaN | 16.66 | 1.1666 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN |
| 279 | 2 | GROWiT | 40 | 280 | 2-Jan-23 | 10-Mar-23 | 24-Feb-23 | Creating Valuable Businesses | Rahul Dua | Agriculture | Protective farming products | https://thegrowit.com/ | <NA> | 2 | 2 | <NA> | <NA> | 0 | Middle | Surat | Gujarat | <NA> | NaN | 22 | <NA> | ... | NaN | NaN | NaN | 25.00 | 0.500 | 25.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN |
| 284 | 2 | ProostBeer | 42 | 285 | 2-Jan-23 | 10-Mar-23 | 28-Feb-23 | Building Businesses From Scratch | Rahul Dua | Liquor/Beverages | Freshly brewed beer | https://www.proost69.com/ | 2018 | 2 | 2 | <NA> | <NA> | 0 | Middle | Delhi | Delhi | <NA> | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN |
| 292 | 2 | HealthyMaster | 44 | 293 | 2-Jan-23 | 10-Mar-23 | 2-Mar-23 | Entrepreneurship Ka Junoon | Rahul Dua | Food | Online Dry Fruits, Snacks, Berries, Chips | https://healthymaster.in/ | 2019 | 3 | 1 | 2 | <NA> | 1 | Middle | Bangalore | Karnataka | <NA> | 20.0 | 45 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 302 | 2 | TheHealthyFactory | 46 | 303 | 2-Jan-23 | 10-Mar-23 | 6-Mar-23 | Different Colours Of Entrepreneurship | Rahul Dua | Food | Protein bread | https://www.thehealthfactory.in/ | 2018 | 2 | 2 | <NA> | <NA> | 0 | Young | Mumbai | Maharashtra | <NA> | NaN | 56 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 309 | 2 | LilGoodness | 48 | 310 | 2-Jan-23 | 10-Mar-23 | 8-Mar-23 | Pitchers, Investments And Businesses | Rahul Dua | Food | Healthy Snacks | https://lilgoodness.com/ | 2020 | 2 | 2 | <NA> | <NA> | 0 | Middle | Bangalore | Karnataka | <NA> | 145.0 | 65 | <NA> | ... | NaN | NaN | NaN | 50.00 | 1.000 | 50.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN |
| 312 | 2 | WickedGud | 49 | 313 | 2-Jan-23 | 10-Mar-23 | 9-Mar-23 | Businesses Adding Value To Society | Rahul Dua | Food | High Protein & Fiber Gluten Free Vegan food | https://wickedgud.com/ | 2021 | 3 | 3 | <NA> | <NA> | 0 | Middle | Mumbai | Maharashtra | <NA> | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN |
| 315 | 2 | MYBYK | 50 | 316 | 2-Jan-23 | 10-Mar-23 | 10-Mar-23 | Season Finale With The Sharks | Rahul Dua | Vehicles/Electrical Vehicles | IoT-enabled bikes | https://mybyk.in/ | <NA> | 1 | 1 | <NA> | <NA> | 0 | Middle | Ahmedabad | Gujarat | <NA> | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN |
| 316 | 2 | GODESi | 51 | 317 | 2-Jan-23 | 10-Mar-23 | 10-Mar-23 | Gateway To Shark Tank India | Rahul Dua | Food | Handmade lollipops | https://godesi.in/ | <NA> | 2 | 1 | 1 | <NA> | 0 | Middle | Bangalore | Karnataka | <NA> | 270.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Vikas D Nahar | 1.0 | 1.0 | NaN | 1.0 | NaN | 1.0 | NaN | 1.0 |
| 317 | 2 | TAC | 51 | 318 | 2-Jan-23 | 10-Mar-23 | 10-Mar-23 | Gateway To Shark Tank India | Rahul Dua | Beauty/Fashion | ayurveda co for glowing skin, makeup & open pores | https://theayurvedaco.com/ | <NA> | 2 | 1 | 1 | <NA> | 1 | Middle | Mumbai | Maharashtra | <NA> | NaN | <NA> | <NA> | ... | 40.50 | 0.5000 | 34.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 40.5 | 0.50 | 34.5 | Vikas D Nahar | Vikas D Nahar | 1.0 | 1.0 | NaN | 1.0 | NaN | 1.0 | NaN | 1.0 |
| 320 | 2 | ZenOnco | 0 | 321 | 2-Jan-23 | 10-Mar-23 | NaN | Unseen | Rahul Dua | Medical/Health | saving lives from cancer | https://zenonco.io/ | <NA> | 2 | 1 | 1 | <NA> | 0 | Middle | Jodhpur | Rajasthan | <NA> | 21.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Vikas D Nahar | 1.0 | 1.0 | NaN | 1.0 | NaN | 1.0 | NaN | 1.0 |
| 322 | 3 | AdilQadri | 1 | 323 | 22-Jan-24 | NaN | 22-Jan-24 | Bigger Better and Smarter | Rahul Dua | Beauty/Fashion | Perfumes and Attar | https://www.adilqadri.com/ | 2020 | 1 | 1 | <NA> | <NA> | 0 | Young | Bilimora | Gujarat | 2070 | 600.0 | 70 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | NaN |
| 327 | 3 | RodBez | 3 | 328 | 22-Jan-24 | NaN | 24-Jan-24 | Quest For Investment | Rahul Dua | Vehicles/Electrical Vehicles | Taxi-service for Bihar | https://rodbez.in/ | 2022 | 2 | 2 | <NA> | <NA> | 0 | Middle | Saharsa | Bihar | <NA> | 6.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.0 | 2.50 | 15.0 | Ritesh Aggarwal | Ritesh Aggarwal | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 |
| 329 | 3 | Homversity | 3 | 330 | 22-Jan-24 | NaN | 24-Jan-24 | Quest For Investment | Rahul Dua | Technology/Software | Digital student housing app | https://www.homversity.com/ | 2022 | 1 | 1 | <NA> | <NA> | 0 | Young | Ahmedabad | Gujarat | <NA> | 100.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Ritesh Aggarwal | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 |
| 335 | 3 | Bartisans | 5 | 336 | 22-Jan-24 | NaN | 26-Jan-24 | Innovative Ventures Vie For Sharks' Favour | Rahul Dua | Liquor/Beverages | Cocktail Mocktail mixers | https://www.bartisans.in/ | 2021 | 2 | 1 | 1 | <NA> | 0 | Middle | Mumbai | Maharashtra | 143 | 35.0 | 70 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Ritesh Aggarwal,Radhika Gupta | NaN | 1.0 | NaN | 1.0 | 1.0 | NaN | NaN | 2.0 |
| 336 | 3 | Aretto | 6 | 337 | 22-Jan-24 | NaN | 29-Jan-24 | Nurturing The Spirit Of Entrepreneurship | Rahul Dua | Manufacturing | Expandable Shoes For Kids | https://wearetto.com/ | 2020 | 1 | 1 | <NA> | <NA> | 0 | Middle | Pune | Maharashtra | 700 | 60.0 | 57 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 343 | 3 | WYLDCard | 8 | 344 | 22-Jan-24 | NaN | 31-Jan-24 | Entrepreneurial Innovation | Rahul Dua | Technology/Software | Customer as Infulencer | https://www.getwyld.in/ | 2023 | 3 | 3 | <NA> | <NA> | 0 | Middle | Mumbai | Maharashtra | <NA> | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Azhar Iqubal | 1.0 | NaN | 1.0 | NaN | 1.0 | 1.0 | NaN | 1.0 |
| 344 | 3 | upliance.ai | 8 | 345 | 22-Jan-24 | NaN | 31-Jan-24 | Entrepreneurial Innovation | Rahul Dua | Furnishing/Household | Smart Cooker | upliance.ai | 2021 | 2 | 2 | <NA> | <NA> | 0 | Middle | Bangalore | Karnataka | <NA> | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Azhar Iqubal | 1.0 | NaN | 1.0 | NaN | 1.0 | 1.0 | NaN | 1.0 |
| 347 | 3 | RooftopApp | 9 | 348 | 22-Jan-24 | NaN | 1-Feb-24 | Entrepreneurial Brilliance | Rahul Dua | Technology/Software | Art Learning platform | https://rooftopapp.com/ | 2019 | 1 | 1 | <NA> | <NA> | 0 | Middle | Mumbai | Maharashtra | <NA> | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Deepinder Goyal | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN | 1.0 |
| 352 | 3 | EvaScalp | 11 | 353 | 22-Jan-24 | NaN | 5-Feb-24 | Disrupting The Status Quo | Rahul Dua | Medical/Health | Post Chemo Scalp Cooling brand | https://evascalpcooling.co.in/ | 2020 | 1 | 1 | <NA> | <NA> | 0 | Middle | Mumbai | Maharashtra | 59 | 10.0 | 85 | <NA> | ... | 10.00 | 0.6000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.0 | 0.60 | NaN | Ritesh Aggarwal | Ritesh Aggarwal | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN | 1.0 |
| 353 | 3 | Elitty | 11 | 354 | 22-Jan-24 | NaN | 5-Feb-24 | Disrupting The Status Quo | Rahul Dua | Beauty/Fashion | Teenage Make-up | https://elittybeauty.com/ | <NA> | 2 | <NA> | 2 | <NA> | 0 | Middle | Gurgaon | Haryana | 113 | 11.0 | 36 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Ritesh Aggarwal | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN | 1.0 |
| 362 | 3 | DecodeAge | 14 | 363 | 22-Jan-24 | NaN | 8-Feb-24 | Pitch Perfect | Rahul Dua | Medical/Health | Age Longetivity Supplements | https://decodeage.com/ | 2021 | 3 | 3 | <NA> | <NA> | 0 | Middle | Bangalore | Karnataka | 1100 | NaN | 70 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | NaN |
| 366 | 3 | AltCo | 16 | 367 | 22-Jan-24 | NaN | 12-Feb-24 | Innovation At Every Step | Rahul Dua | Food | Plant based dairy products | https://alt.company/ | 2020 | 2 | 2 | <NA> | <NA> | 0 | Middle | Bangalore | Karnataka | 1000 | 110.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Ronnie Screwvala,Radhika Gupta | NaN | NaN | 1.0 | 1.0 | 1.0 | NaN | NaN | 2.0 |
| 377 | 3 | ToffeeCoffeeRoasters | 19 | 378 | 22-Jan-24 | NaN | 15-Feb-24 | The Next Big Investment | Rahul Dua | Liquor/Beverages | Coffee brand | https://toffeecoffeeroasters.com/ | 2019 | 2 | 1 | 1 | <NA> | 1 | Middle | Bangalore | Karnataka | 230 | 33.0 | 62 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 35.0 | 2.33 | 25.0 | Ritesh Aggarwal | Ritesh Aggarwal,Radhika Gupta | NaN | 1.0 | NaN | 1.0 | 1.0 | NaN | NaN | 2.0 |
| 379 | 3 | ORBO | 20 | 380 | 22-Jan-24 | NaN | 16-Feb-24 | Pioneering Change | Rahul Dua | Technology/Software | AI-powered tools for beauty brands | https://www.orbo.ai/ | 2019 | 3 | 3 | <NA> | <NA> | 0 | Middle | Mumbai | Maharashtra | <NA> | 18.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Azhar Iqubal | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 |
| 384 | 3 | D'chica | 22 | 385 | 22-Jan-24 | NaN | 20-Feb-24 | Impressive Numbers and High Stakes | Rahul Dua | Beauty/Fashion | Innerwears for Teenage Girls | https://www.dchica.in/ | 2020 | 2 | <NA> | 2 | <NA> | 0 | Middle | Delhi | Delhi | 1030 | 120.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | NaN |
| 387 | 3 | Artinci | 23 | 388 | 22-Jan-24 | NaN | 21-Feb-24 | Celebrating Entrepreneurial Breakthroughs | Rahul Dua | Food | Zero Sugar Desserts | https://www.artinci.com/ | 2017 | 2 | 1 | 1 | <NA> | 1 | Middle | Bangalore | Karnataka | 440 | 33.0 | 62 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 396 | 3 | Aroleap | 26 | 397 | 22-Jan-24 | NaN | 26-Feb-24 | Entrepreneurship On The Rise | Rahul Dua | Medical/Health | Smart Home Gym | https://www.aroleap.com/ | 2020 | 3 | 3 | <NA> | <NA> | 0 | Middle | Bangalore | Karnataka | 100 | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | 25.00 | 1.250 | NaN | 25.00 | 1.2500 | NaN | NaN | NaN | NaN | 25.0 | 1.25 | NaN | Azhar Iqubal | Azhar Iqubal | 1.0 | NaN | 1.0 | NaN | 1.0 | 1.0 | NaN | 1.0 |
| 408 | 3 | AvataarSkincare | 30 | 409 | 22-Jan-24 | NaN | 1-Mar-24 | Startups Pursuing Investment | Rahul Dua | Beauty/Fashion | Skincare Services | https://avataarskin.com/ | 2022 | 1 | <NA> | 1 | <NA> | 0 | Middle | Bhopal | Madhya Pradesh | <NA> | 60.0 | 87 | <NA> | ... | 17.50 | 0.2500 | 17.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN |
| 421 | 3 | Niblerzz | 34 | 422 | 22-Jan-24 | NaN | 7-Mar-24 | Visionary Brands Shine Bright | Rahul Dua | Food | Sugar Free Candy | https://niblerzz.com/ | 2022 | 2 | <NA> | 2 | <NA> | 0 | Middle | Mumbai | Maharashtra | 16 | 5.5 | <NA> | <NA> | ... | 10.00 | 5.0000 | 40.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Azhar Iqubal | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 |
| 422 | 3 | Sorich | 34 | 423 | 22-Jan-24 | NaN | 7-Mar-24 | Visionary Brands Shine Bright | Rahul Dua | Food | Guilt free Snacks | https://sorichorganics.com/ | <NA> | 2 | 1 | 1 | <NA> | 1 | Middle | Delhi | Delhi | 706 | 55.0 | 48 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Azhar Iqubal | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 |
| 424 | 3 | CremeCastle | 35 | 425 | 22-Jan-24 | NaN | 8-Mar-24 | Inspiring Women Entrepreneurs | Rahul Dua | Food | Customised Cakes and bakery products | https://cremecastle.in/ | 2015 | 2 | 1 | 1 | <NA> | 1 | Middle | Noida | Uttar Pradesh | 720 | NaN | 65 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | 60.00 | 2.5000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | NaN |
| 432 | 3 | Cup-ji | 38 | 433 | 22-Jan-24 | NaN | 13-Mar-24 | Thoughts And Innovations | Rahul Dua | Liquor/Beverages | Flavoured Instant Tea in Cups | https://cupji.com/ | 2022 | 2 | 2 | <NA> | <NA> | 0 | Middle | Mumbai | Maharashtra | 62 | 3.5 | 40 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | NaN |
| 433 | 3 | AToddlerThing | 38 | 434 | 22-Jan-24 | NaN | 13-Mar-24 | Thoughts And Innovations | Rahul Dua | Beauty/Fashion | Baby Clothing and Essentials | https://www.atoddlerthing.com/ | 2017 | 2 | 1 | 1 | <NA> | 0 | Middle | Coimbatore | Tamil Nadu | 478 | 73.0 | 52 | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | 40.00 | 2.0000 | 40.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | NaN |
| 436 | 3 | iDreamCareer | 39 | 437 | 22-Jan-24 | NaN | 14-Mar-24 | Sustainability Careers And Spirits | Rahul Dua | Technology/Software | Career Counseling Platform | https://idreamcareer.com/ | 2012 | 2 | 2 | <NA> | <NA> | 0 | Middle | Delhi,Bangalore | Delhi,Karnataka | 840 | NaN | <NA> | <NA> | ... | 30.00 | 0.5000 | 25.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 30.0 | 0.50 | 25.0 | Ritesh Aggarwal | Ritesh Aggarwal | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 |
| 437 | 3 | RockPaperRum | 39 | 438 | 22-Jan-24 | NaN | 14-Mar-24 | Sustainability Careers And Spirits | Rahul Dua | Liquor/Beverages | Innovative Indian Rum | https://www.rockpaperrum.com/ | 2022 | 1 | 1 | <NA> | <NA> | 0 | Middle | Mumbai | Maharashtra | <NA> | NaN | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Ritesh Aggarwal | NaN | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | 1.0 |
| 439 | 3 | Sukham | 40 | 440 | 22-Jan-24 | NaN | 15-Mar-24 | Shaping A Healthier Future | Rahul Dua | Medical/Health | Holistic sexual male wellness | https://www.sukham.life/ | <NA> | 4 | 3 | 1 | <NA> | 1 | Middle | Delhi | Delhi | 47 | 21.0 | <NA> | <NA> | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Deepinder Goyal | 1.0 | 1.0 | 1.0 | 1.0 | NaN | NaN | NaN | 1.0 |
56 rows × 78 columns
# Top 15 Highest Yearly Revenue brands, in all seasons
print(shark_tank.groupby('Startup Name')['Yearly Revenue'].max().nlargest(15))
tmpdf = shark_tank.sort_values('Yearly Revenue', ascending=False)[0:15]
fig = px.bar(tmpdf, x="Startup Name", y='Yearly Revenue', color="Startup Name", template='simple_white', title="<b>Highest revenue (in lakhs) of participated startups, in all seasons</b>", text=tmpdf['Yearly Revenue'])
fig.show()
Startup Name FrenchCrown 7200.0 Rubans 5100.0 Toyshine 4500.0 GuardianGears 2500.0 GunjanAppsStudios 2400.0 UnStop 1600.0 StyloBug 1400.0 RaisingSuperstars 1300.0 DesmondJi 1200.0 Eume 1200.0 PlayBoxTV 1020.0 oyehappy 1005.0 Alpino 1000.0 BlueTea 1000.0 HammerLifestyle 1000.0 Name: Yearly Revenue, dtype: float64
# Top 10 Highest Yearly Revenue brands, in latest/current season (3rd season)
print(shark_tank_season3.groupby('Startup Name')['Yearly Revenue'].max().nlargest(10))
tmpdf = shark_tank_season3.sort_values('Yearly Revenue', ascending=False)[0:10]
fig = px.bar(tmpdf, x="Startup Name", y='Yearly Revenue', color="Startup Name", template='simple_white', title="<b>Highest revenue (in lakhs) of participated startups, in Season 3</b>", text=tmpdf['Yearly Revenue'])
fig.show()
Startup Name Refit 18700 NasherMiles 5700 YesMadam 5000 BaccaBucci 4700 LittleBox 3600 Zorko 3000 AdilQadri 2070 UrbanSpace 2050 HonestHome 1400 UnclePetersPanCakes 1400 Name: Yearly Revenue, dtype: Int32
# Filter data for the 3rd season
season3_data = df[df['Season Number'] == 3]
# Top 15 highest Gross Margin brands, in all seasons
print(shark_tank.groupby('Startup Name')['Gross Margin'].max().nlargest(15))
tmpdf = shark_tank.sort_values('Gross Margin', ascending=False)[0:15]
fig = px.bar(tmpdf, x="Startup Name", y='Gross Margin', color="Startup Name", template='simple_white', title="<b>Highest Gross margin (in %) of the brands (in all seasons)</b>", text=tmpdf['Gross Margin'].map(int).map(str) + "%")
fig.show()
Startup Name Poo-de-Cologne 150.0 Farda 115.0 Cocofit 95.0 UnStop 90.0 MidNightAngelsByPC 83.0 Auli 80.0 LeafyAffair 80.0 Pflow 80.0 ekatra 80.0 oyehappy 80.0 CosIQ 75.0 Dabble 75.0 JaipurWatchCompany 75.0 TheaandSid 75.0 Bummer 70.0 Name: Gross Margin, dtype: float64
# Top 15 highest Net Margin brands, in all seasons
print(shark_tank.groupby('Startup Name')['Net Margin'].max().nlargest(15))
tmpdf = shark_tank.sort_values('Net Margin', ascending=False)[0:15]
fig = px.bar(tmpdf, x="Startup Name", y='Net Margin', color="Startup Name", template='simple_white', title="<b>Highest Net margin (in %) of the brands</b>", text=tmpdf['Net Margin'].map(int).map(str) + "%")
fig.show()
Startup Name Cakelicious 40.0 TwistingScoops 40.0 SharmaJiKiAata 38.0 DrCubes 35.0 Pabiben 35.0 VAPerfume 35.0 NishHair 30.0 UpThrust 30.0 ekatra 28.0 Tipayi 26.0 Flhexible 25.0 MeduLance 24.0 eyenic 21.0 Febris 20.0 MidNightAngelsByPC 20.0 Name: Net Margin, dtype: float64
# Word cloud based on Business Description of startups came in all seasons
text = " Shark Tank India ".join(cat for cat in shark_tank.loc[shark_tank['Business Description'].notnull()]['Business Description'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='salmon', colormap='Pastel1', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(20,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
# Word cloud based on Business Description, startups came in current/latest season (3rd season)
text = " Shark Tank India ".join(cat for cat in shark_tank_season3.loc[shark_tank_season3['Business Description'].notnull()]['Business Description'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='salmon', colormap='Pastel2', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(20,16))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
# Correlation matrix
shark_tank.corr(numeric_only=True).style.background_gradient(cmap = 'Blues')
| Season Number | Episode Number | Pitch Number | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | EBITDA | SKUs | Original Ask Amount | Original Offered Equity | Valuation Requested | Received Offer | Accepted Offer | Total Deal Amount | Total Deal Equity | Total Deal Debt | Debt Interest | Deal Valuation | Number of Sharks in Deal | Royalty Deal | Advisory Shares Equity | Namita Investment Amount | Namita Investment Equity | Namita Debt Amount | Vineeta Investment Amount | Vineeta Investment Equity | Vineeta Debt Amount | Anupam Investment Amount | Anupam Investment Equity | Anupam Debt Amount | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Season Number | 1.000000 | 0.165993 | 0.938991 | 0.212098 | -0.073709 | -0.019409 | 0.035600 | nan | -0.033453 | 0.161995 | 0.186585 | 0.038127 | -0.171579 | nan | -0.152633 | -0.048222 | -0.222444 | 0.081853 | 0.061306 | 0.134256 | 0.150178 | -0.432962 | 0.081566 | 0.011851 | 0.293433 | -0.074752 | nan | nan | 0.162246 | -0.280535 | 0.228971 | 0.348674 | -0.531534 | -0.131312 | 0.222332 | -0.376758 | 0.317895 | 0.083637 | -0.290386 | -0.027655 | 0.189545 | -0.273924 | 0.480248 | -0.096492 | -0.194977 | 0.103882 | nan | nan | nan | 0.270924 | -0.402261 | 0.268711 | nan | nan | nan | nan | nan | nan | nan | 0.257709 |
| Episode Number | 0.165993 | 1.000000 | 0.388181 | -0.001709 | 0.028587 | 0.060830 | 0.040278 | nan | -0.020922 | 0.053446 | 0.014307 | 0.025498 | 0.224991 | -0.100959 | 0.104549 | -0.030911 | -0.072443 | 0.036941 | -0.017463 | 0.071186 | -0.102776 | -0.118867 | -0.010375 | 0.189114 | -0.018649 | 0.019458 | nan | -0.989457 | -0.049936 | -0.066545 | -0.250550 | -0.140468 | -0.285217 | -0.140762 | -0.158157 | -0.192655 | -0.107538 | -0.034936 | -0.141009 | -0.438680 | -0.066868 | 0.016492 | 0.185670 | -0.172148 | -0.258796 | 0.946028 | -0.266455 | -0.182477 | 1.000000 | -0.347703 | -0.070913 | 0.167497 | nan | nan | nan | nan | nan | nan | nan | -0.126931 |
| Pitch Number | 0.938991 | 0.388181 | 1.000000 | 0.200920 | -0.065776 | -0.019511 | 0.003186 | nan | -0.026957 | 0.168618 | 0.176293 | 0.038012 | -0.106616 | -0.088741 | -0.114034 | -0.061585 | -0.203165 | 0.075639 | -0.025026 | 0.096432 | 0.105442 | -0.430507 | 0.065766 | 0.110168 | 0.262281 | -0.060060 | nan | -0.997631 | 0.124369 | -0.269117 | 0.001822 | 0.277413 | -0.593869 | -0.176980 | 0.152515 | -0.395067 | 0.233881 | 0.062384 | -0.299713 | -0.193498 | 0.167375 | -0.256414 | 0.460870 | -0.179683 | -0.330110 | 0.510863 | -0.261785 | -0.163796 | 1.000000 | 0.173347 | -0.449065 | 0.565519 | nan | nan | nan | nan | nan | nan | nan | 0.228354 |
| Started in | 0.212098 | -0.001709 | 0.200920 | 1.000000 | -0.082014 | 0.042050 | 0.065528 | nan | -0.180592 | -0.117439 | -0.061515 | 0.372268 | 0.258344 | -0.089895 | -0.334776 | -0.103409 | -0.042775 | -0.099580 | 0.120736 | 0.143688 | -0.024553 | -0.031207 | -0.024675 | 0.167881 | -0.086094 | 0.125226 | nan | 0.796448 | -0.201499 | -0.257600 | -0.269031 | -0.201068 | -0.079121 | 0.377058 | 0.077847 | -0.013993 | 0.574076 | -0.147026 | -0.032074 | 0.014568 | -0.284248 | -0.117426 | 0.071980 | -0.421349 | 0.035042 | -0.544331 | 0.145770 | -0.332778 | 1.000000 | 0.235303 | 0.150895 | 0.303579 | nan | nan | nan | nan | nan | nan | nan | 0.069559 |
| Number of Presenters | -0.073709 | 0.028587 | -0.065776 | -0.082014 | 1.000000 | 0.757998 | 0.283863 | nan | 0.176139 | 0.019579 | -0.001270 | -0.206100 | 0.066237 | -0.299121 | -0.024387 | -0.053648 | -0.141338 | 0.066529 | 0.012508 | -0.054645 | 0.099664 | -0.190931 | 0.152315 | 0.123101 | 0.128628 | 0.068547 | nan | -0.880812 | -0.007147 | -0.093339 | -0.265797 | 0.148447 | -0.190457 | -0.279278 | 0.120184 | -0.237157 | -0.280335 | 0.076863 | -0.002672 | 0.339361 | 0.024934 | -0.275373 | 0.501527 | -0.199916 | -0.108593 | 0.722705 | -0.078089 | -0.278986 | nan | -0.036984 | -0.230962 | -0.952982 | nan | nan | nan | nan | nan | nan | nan | -0.038172 |
| Male Presenters | -0.019409 | 0.060830 | -0.019511 | 0.042050 | 0.757998 | 1.000000 | 0.033014 | nan | -0.309805 | 0.046550 | 0.026806 | -0.120141 | 0.143627 | -0.336476 | 0.097058 | -0.038795 | -0.153961 | 0.066412 | 0.008411 | 0.051746 | 0.093616 | -0.131314 | 0.250178 | 0.106095 | 0.191994 | 0.066619 | nan | nan | -0.005148 | -0.145149 | 0.251081 | 0.065770 | -0.110525 | 0.040427 | 0.119344 | -0.207503 | 0.113837 | 0.183658 | 0.025831 | -0.059643 | 0.024584 | -0.192138 | 0.382420 | -0.111801 | -0.297954 | 0.760469 | -0.169740 | -0.169891 | 1.000000 | -0.179377 | -0.241198 | -0.372594 | nan | nan | nan | nan | nan | nan | nan | -0.089400 |
| Female Presenters | 0.035600 | 0.040278 | 0.003186 | 0.065528 | 0.283863 | 0.033014 | 1.000000 | nan | -0.111463 | -0.134308 | -0.042923 | 0.058179 | 0.123260 | -0.606177 | -0.169992 | -0.063666 | -0.001856 | 0.023272 | 0.046768 | -0.122638 | -0.188664 | 0.013347 | -0.103112 | -0.302962 | -0.124493 | -0.155053 | nan | nan | -0.166104 | -0.135065 | -0.591373 | 0.004639 | -0.084876 | -0.382282 | -0.068333 | 0.057210 | nan | 0.106950 | 0.338261 | 0.195316 | -0.189834 | 0.009135 | -0.628539 | -0.157921 | 0.663184 | -0.711328 | nan | nan | nan | -0.099306 | 0.105189 | nan | nan | nan | nan | nan | nan | nan | nan | 0.047938 |
| Transgender Presenters | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Couple Presenters | -0.033453 | -0.020922 | -0.026957 | -0.180592 | 0.176139 | -0.309805 | -0.111463 | nan | 1.000000 | 0.029558 | -0.006296 | -0.131371 | -0.088358 | 0.079286 | -0.077149 | -0.019618 | -0.026054 | 0.044130 | -0.051843 | -0.073623 | 0.081265 | -0.047517 | -0.128943 | 0.069248 | 0.017568 | -0.024258 | nan | nan | 0.158871 | 0.183871 | -0.393045 | 0.225503 | -0.022463 | -0.364986 | 0.064985 | -0.081840 | -0.439941 | 0.040536 | -0.081253 | 0.021341 | 0.066791 | -0.023587 | -0.131753 | 0.065055 | -0.134876 | -0.328502 | 0.311533 | -0.147427 | -1.000000 | -0.019907 | -0.203369 | -0.359027 | nan | nan | nan | nan | nan | nan | nan | 0.021327 |
| Yearly Revenue | 0.161995 | 0.053446 | 0.168618 | -0.117439 | 0.019579 | 0.046550 | -0.134308 | nan | 0.029558 | 1.000000 | 0.960723 | -0.196654 | -0.099937 | 0.047336 | 0.635944 | -0.012668 | -0.241477 | 0.420723 | 0.089209 | 0.007592 | 0.426605 | -0.180696 | 0.109087 | 0.119941 | 0.570696 | 0.238430 | nan | 1.000000 | 0.093676 | -0.277898 | -0.192972 | 0.394862 | -0.299970 | -0.242940 | 0.301259 | -0.264331 | 0.245613 | 0.089555 | -0.129570 | -0.512487 | 0.015215 | -0.200131 | 0.000000 | 0.224041 | -0.329391 | 0.662975 | 0.649849 | -0.291415 | nan | 0.156418 | -0.372766 | 0.298133 | nan | nan | nan | nan | nan | nan | nan | 0.051283 |
| Monthly Sales | 0.186585 | 0.014307 | 0.176293 | -0.061515 | -0.001270 | 0.026806 | -0.042923 | nan | -0.006296 | 0.960723 | 1.000000 | -0.222777 | -0.293741 | 0.410348 | 0.101299 | -0.003653 | -0.186564 | 0.329484 | 0.054820 | -0.014604 | 0.397487 | -0.148271 | 0.197484 | 0.441757 | 0.580422 | 0.041845 | nan | -1.000000 | 0.138890 | -0.333172 | 0.313564 | 0.306777 | -0.217983 | 0.331079 | 0.395109 | -0.179823 | 0.318739 | 0.565563 | -0.309680 | -0.468403 | 0.327598 | -0.144140 | 0.949158 | 0.247172 | -0.271093 | 0.587076 | 0.151069 | -0.460915 | nan | 0.608080 | -0.335827 | 0.917171 | nan | nan | nan | nan | nan | nan | nan | 0.008203 |
| Gross Margin | 0.038127 | 0.025498 | 0.038012 | 0.372268 | -0.206100 | -0.120141 | 0.058179 | nan | -0.131371 | -0.196654 | -0.222777 | 1.000000 | 0.334639 | 0.363241 | -0.843233 | -0.150168 | -0.030419 | -0.138236 | -0.015930 | 0.186509 | -0.147143 | 0.030137 | -0.036532 | 0.162457 | -0.127319 | 0.114566 | nan | nan | -0.068330 | -0.004651 | 0.222146 | 0.055460 | -0.092342 | 0.211237 | -0.052256 | -0.052607 | 0.533769 | -0.279248 | 0.026779 | -0.864464 | -0.229211 | 0.077915 | -0.057992 | -0.604607 | 0.099229 | -1.000000 | -0.670061 | 0.707695 | nan | -0.355616 | -0.346967 | 0.508576 | nan | nan | nan | nan | nan | nan | nan | 0.257626 |
| Net Margin | -0.171579 | 0.224991 | -0.106616 | 0.258344 | 0.066237 | 0.143627 | 0.123260 | nan | -0.088358 | -0.099937 | -0.293741 | 0.334639 | 1.000000 | -0.720577 | -0.453014 | -0.138014 | 0.460646 | -0.205430 | 0.055656 | 0.212698 | -0.341449 | 0.516857 | 0.466627 | -0.893309 | -0.280411 | 0.018103 | nan | nan | -0.604944 | -0.047809 | nan | -0.307421 | 0.306428 | nan | -0.157330 | 0.660418 | nan | 0.033958 | 0.438284 | 0.973684 | -0.310531 | 0.326559 | nan | -0.529238 | 0.358334 | -1.000000 | nan | nan | nan | -0.516589 | 0.767058 | nan | nan | nan | nan | nan | nan | nan | nan | 0.175889 |
| EBITDA | nan | -0.100959 | -0.088741 | -0.089895 | -0.299121 | -0.336476 | -0.606177 | nan | 0.079286 | 0.047336 | 0.410348 | 0.363241 | -0.720577 | 1.000000 | -0.130973 | 0.252753 | -0.092894 | 0.042547 | 0.093900 | 0.152896 | 0.400438 | 0.067488 | 0.793677 | 0.215274 | 0.046667 | 0.242409 | nan | nan | -0.303043 | -0.236433 | nan | 0.998137 | 0.999554 | nan | 0.265481 | 0.033219 | nan | 0.568731 | -0.675184 | 1.000000 | nan | nan | nan | -0.086588 | 0.363713 | nan | nan | nan | nan | 1.000000 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | 0.666776 |
| SKUs | -0.152633 | 0.104549 | -0.114034 | -0.334776 | -0.024387 | 0.097058 | -0.169992 | nan | -0.077149 | 0.635944 | 0.101299 | -0.843233 | -0.453014 | -0.130973 | 1.000000 | 0.025972 | -0.270435 | 0.250590 | 0.164240 | 0.073754 | -0.087194 | -0.230059 | -0.871817 | nan | 0.019151 | -0.190829 | nan | nan | -1.000000 | 1.000000 | nan | 0.812178 | -0.500000 | nan | 0.071019 | 0.699011 | nan | -0.257537 | -0.085186 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | -0.109575 |
| Original Ask Amount | -0.048222 | -0.030911 | -0.061585 | -0.103409 | -0.053648 | -0.038795 | -0.063666 | nan | -0.019618 | -0.012668 | -0.003653 | -0.150168 | -0.138014 | 0.252753 | 0.025972 | 1.000000 | 0.266994 | 0.614736 | -0.076332 | -0.231998 | 0.718948 | -0.297513 | 0.875036 | 0.160046 | 0.744812 | 0.077581 | nan | -0.269565 | 0.485975 | -0.251609 | 0.291903 | 0.549843 | -0.266759 | 0.515546 | 0.510099 | -0.209983 | 0.719931 | 0.464068 | -0.238795 | 0.199305 | 0.587167 | -0.210585 | 0.518195 | 0.531077 | -0.425723 | 0.763852 | 0.385142 | -0.179681 | 1.000000 | 0.360845 | -0.168632 | 0.891364 | nan | nan | nan | nan | nan | nan | nan | -0.034174 |
| Original Offered Equity | -0.222444 | -0.072443 | -0.203165 | -0.042775 | -0.141338 | -0.153961 | -0.001856 | nan | -0.026054 | -0.241477 | -0.186564 | -0.030419 | 0.460646 | -0.092894 | -0.270435 | 0.266994 | 1.000000 | -0.159605 | -0.173207 | 0.071146 | -0.328453 | 0.686292 | -0.191976 | -0.406454 | -0.404858 | 0.025618 | nan | 0.030373 | -0.277727 | 0.632737 | 0.494519 | -0.426339 | 0.620137 | -0.152513 | -0.310447 | 0.532555 | -0.166422 | -0.365176 | 0.334861 | 0.135962 | -0.198120 | 0.560722 | -0.637381 | -0.544839 | 0.301131 | -0.781745 | -0.350304 | 0.766988 | -1.000000 | -0.237020 | 0.546484 | 0.187940 | nan | nan | nan | nan | nan | nan | nan | -0.189596 |
| Valuation Requested | 0.081853 | 0.036941 | 0.075639 | -0.099580 | 0.066529 | 0.066412 | 0.023272 | nan | 0.044130 | 0.420723 | 0.329484 | -0.138236 | -0.205430 | 0.042547 | 0.250590 | 0.614736 | -0.159605 | 1.000000 | 0.013093 | -0.136789 | 0.550233 | -0.359464 | 0.376454 | 0.186514 | 0.826239 | 0.116913 | nan | -0.209162 | 0.312662 | -0.343628 | -0.204553 | 0.475518 | -0.378316 | -0.087602 | 0.396726 | -0.303209 | 0.502577 | 0.313034 | -0.279927 | -0.211592 | 0.343128 | -0.245236 | 0.270917 | 0.342471 | -0.308730 | 0.591304 | 0.356795 | -0.344763 | 1.000000 | 0.187570 | -0.345155 | 0.103562 | nan | nan | nan | nan | nan | nan | nan | 0.037627 |
| Received Offer | 0.061306 | -0.017463 | -0.025026 | 0.120736 | 0.012508 | 0.008411 | 0.046768 | nan | -0.051843 | 0.089209 | 0.054820 | -0.015930 | 0.055656 | 0.093900 | 0.164240 | -0.076332 | -0.173207 | 0.013093 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.104706 |
| Accepted Offer | 0.134256 | 0.071186 | 0.096432 | 0.143688 | -0.054645 | 0.051746 | -0.122638 | nan | -0.073623 | 0.007592 | -0.014604 | 0.186509 | 0.212698 | 0.152896 | 0.073754 | -0.231998 | 0.071146 | -0.136789 | nan | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.067850 |
| Total Deal Amount | 0.150178 | -0.102776 | 0.105442 | -0.024553 | 0.099664 | 0.093616 | -0.188664 | nan | 0.081265 | 0.426605 | 0.397487 | -0.147143 | -0.341449 | 0.400438 | -0.087194 | 0.718948 | -0.328453 | 0.550233 | nan | nan | 1.000000 | -0.208890 | 0.539594 | 0.200191 | 0.663985 | 0.327959 | nan | -0.030373 | 0.461891 | -0.282010 | 0.044096 | 0.512598 | -0.381410 | 0.283410 | 0.519919 | -0.277993 | 0.569578 | 0.510879 | -0.157542 | -0.032224 | 0.615546 | -0.292754 | 0.374517 | 0.497227 | -0.336586 | -0.329343 | 0.610433 | -0.117275 | -1.000000 | 0.661743 | -0.022326 | 0.620285 | nan | nan | nan | nan | nan | nan | nan | 0.135177 |
| Total Deal Equity | -0.432962 | -0.118867 | -0.430507 | -0.031207 | -0.190931 | -0.131314 | 0.013347 | nan | -0.047517 | -0.180696 | -0.148271 | 0.030137 | 0.516857 | 0.067488 | -0.230059 | -0.297513 | 0.686292 | -0.359464 | nan | nan | -0.208890 | 1.000000 | -0.276474 | -0.294494 | -0.383184 | 0.001520 | nan | 0.762187 | -0.207216 | 0.740784 | 0.246986 | -0.347248 | 0.849360 | 0.149519 | -0.188450 | 0.795484 | -0.076059 | -0.142621 | 0.761101 | 0.085786 | -0.174186 | 0.911218 | -0.465025 | -0.360431 | 0.748191 | -0.829397 | -0.234626 | 0.885537 | -1.000000 | -0.079096 | 0.849757 | 0.322929 | nan | nan | nan | nan | nan | nan | nan | -0.189206 |
| Total Deal Debt | 0.081566 | -0.010375 | 0.065766 | -0.024675 | 0.152315 | 0.250178 | -0.103112 | nan | -0.128943 | 0.109087 | 0.197484 | -0.036532 | 0.466627 | 0.793677 | -0.871817 | 0.875036 | -0.191976 | 0.376454 | nan | nan | 0.539594 | -0.276474 | 1.000000 | 0.045565 | 0.515426 | 0.398541 | nan | nan | 0.634659 | -0.151591 | 0.542212 | 0.431945 | -0.099005 | 0.720099 | 0.515652 | 0.008170 | 0.802924 | 0.255011 | -0.389433 | 0.422350 | 0.544555 | -0.448814 | 0.643727 | -0.164013 | -0.823649 | 0.984213 | -1.000000 | -1.000000 | 1.000000 | 0.772072 | 0.341584 | 0.964181 | nan | nan | nan | nan | nan | nan | nan | 0.213265 |
| Debt Interest | 0.011851 | 0.189114 | 0.110168 | 0.167881 | 0.123101 | 0.106095 | -0.302962 | nan | 0.069248 | 0.119941 | 0.441757 | 0.162457 | -0.893309 | 0.215274 | nan | 0.160046 | -0.406454 | 0.186514 | nan | nan | 0.200191 | -0.294494 | 0.045565 | 1.000000 | 0.206331 | 0.136780 | nan | nan | 0.352882 | -0.360717 | -0.115168 | 0.230848 | 0.347957 | -0.004961 | -0.445133 | -0.692034 | 0.392911 | 0.512840 | -0.881064 | -0.207624 | 0.050443 | -0.766907 | -0.453990 | -0.114708 | -0.866025 | 0.976221 | nan | nan | nan | -0.217775 | 0.087682 | -0.081987 | nan | nan | nan | nan | nan | nan | nan | 0.294880 |
| Deal Valuation | 0.293433 | -0.018649 | 0.262281 | -0.086094 | 0.128628 | 0.191994 | -0.124493 | nan | 0.017568 | 0.570696 | 0.580422 | -0.127319 | -0.280411 | 0.046667 | 0.019151 | 0.744812 | -0.404858 | 0.826239 | nan | nan | 0.663985 | -0.383184 | 0.515426 | 0.206331 | 1.000000 | 0.119974 | nan | -0.186359 | 0.363722 | -0.348803 | -0.025920 | 0.529455 | -0.431773 | -0.174686 | 0.384863 | -0.357971 | -0.233960 | 0.357411 | -0.335226 | -0.111052 | 0.643808 | -0.253990 | 0.459398 | 0.403495 | -0.331164 | 0.481815 | 0.695487 | -0.422535 | -1.000000 | 0.268676 | -0.379049 | -0.010826 | nan | nan | nan | nan | nan | nan | nan | 0.004660 |
| Number of Sharks in Deal | -0.074752 | 0.019458 | -0.060060 | 0.125226 | 0.068547 | 0.066619 | -0.155053 | nan | -0.024258 | 0.238430 | 0.041845 | 0.114566 | 0.018103 | 0.242409 | -0.190829 | 0.077581 | 0.025618 | 0.116913 | nan | nan | 0.327959 | 0.001520 | 0.398541 | 0.136780 | 0.119974 | 1.000000 | nan | 0.030373 | -0.448819 | -0.401752 | -0.433988 | -0.419515 | -0.373144 | -0.409094 | -0.473367 | -0.505823 | -0.262826 | -0.435428 | -0.272468 | -0.371674 | -0.366745 | -0.382562 | 0.032769 | -0.394250 | -0.358344 | -0.722705 | -0.430449 | -0.096582 | -1.000000 | -0.296979 | -0.327519 | 0.706672 | nan | nan | nan | nan | nan | nan | nan | 0.021358 |
| Royalty Deal | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Advisory Shares Equity | nan | -0.989457 | -0.997631 | 0.796448 | -0.880812 | nan | nan | nan | nan | 1.000000 | -1.000000 | nan | nan | nan | nan | -0.269565 | 0.030373 | -0.209162 | nan | nan | -0.030373 | 0.762187 | nan | nan | -0.186359 | 0.030373 | nan | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | -1.000000 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.880812 |
| Namita Investment Amount | 0.162246 | -0.049936 | 0.124369 | -0.201499 | -0.007147 | -0.005148 | -0.166104 | nan | 0.158871 | 0.093676 | 0.138890 | -0.068330 | -0.604944 | -0.303043 | -1.000000 | 0.485975 | -0.277727 | 0.312662 | nan | nan | 0.461891 | -0.207216 | 0.634659 | 0.352882 | 0.363722 | -0.448819 | nan | nan | 1.000000 | 0.149906 | 0.594739 | 1.000000 | -0.483917 | 1.000000 | 1.000000 | -0.535728 | nan | 1.000000 | -0.341252 | 1.000000 | 0.906842 | -0.422597 | 1.000000 | 1.000000 | -0.570674 | nan | 1.000000 | 0.178222 | nan | 1.000000 | -0.271580 | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Namita Investment Equity | -0.280535 | -0.066545 | -0.269117 | -0.257600 | -0.093339 | -0.145149 | -0.135065 | nan | 0.183871 | -0.277898 | -0.333172 | -0.004651 | -0.047809 | -0.236433 | 1.000000 | -0.251609 | 0.632737 | -0.343628 | nan | nan | -0.282010 | 0.740784 | -0.151591 | -0.360717 | -0.348803 | -0.401752 | nan | nan | 0.149906 | 1.000000 | 0.438389 | -0.483917 | 1.000000 | -1.000000 | -0.535728 | 1.000000 | nan | -0.341252 | 1.000000 | -1.000000 | -0.473162 | 0.997131 | -0.628619 | -0.570674 | 1.000000 | nan | 0.178222 | 1.000000 | nan | -0.271580 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Namita Debt Amount | 0.228971 | -0.250550 | 0.001822 | -0.269031 | -0.265797 | 0.251081 | -0.591373 | nan | -0.393045 | -0.192972 | 0.313564 | 0.222146 | nan | nan | nan | 0.291903 | 0.494519 | -0.204553 | nan | nan | 0.044096 | 0.246986 | 0.542212 | -0.115168 | -0.025920 | -0.433988 | nan | nan | 0.594739 | 0.438389 | 1.000000 | 1.000000 | -1.000000 | 1.000000 | nan | nan | nan | 1.000000 | -1.000000 | 1.000000 | 1.000000 | -0.628619 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Vineeta Investment Amount | 0.348674 | -0.140468 | 0.277413 | -0.201068 | 0.148447 | 0.065770 | 0.004639 | nan | 0.225503 | 0.394862 | 0.306777 | 0.055460 | -0.307421 | 0.998137 | 0.812178 | 0.549843 | -0.426339 | 0.475518 | nan | nan | 0.512598 | -0.347248 | 0.431945 | 0.230848 | 0.529455 | -0.419515 | nan | nan | 1.000000 | -0.483917 | 1.000000 | 1.000000 | -0.103649 | 0.589324 | 1.000000 | -0.219316 | 1.000000 | 1.000000 | -0.373616 | nan | 0.833330 | -0.292998 | nan | 1.000000 | -0.641754 | nan | 1.000000 | -0.047601 | nan | 0.862329 | -0.273027 | -1.000000 | nan | nan | nan | nan | nan | nan | nan | 0.209982 |
| Vineeta Investment Equity | -0.531534 | -0.285217 | -0.593869 | -0.079121 | -0.190457 | -0.110525 | -0.084876 | nan | -0.022463 | -0.299970 | -0.217983 | -0.092342 | 0.306428 | 0.999554 | -0.500000 | -0.266759 | 0.620137 | -0.378316 | nan | nan | -0.381410 | 0.849360 | -0.099005 | 0.347957 | -0.431773 | -0.373144 | nan | nan | -0.483917 | 1.000000 | -1.000000 | -0.103649 | 1.000000 | 0.275393 | -0.219316 | 1.000000 | -1.000000 | -0.373616 | 1.000000 | nan | -0.359624 | 0.998008 | nan | -0.641754 | 1.000000 | nan | -0.047601 | 1.000000 | nan | -0.360294 | 0.992607 | nan | nan | nan | nan | nan | nan | nan | nan | -0.303400 |
| Vineeta Debt Amount | -0.131312 | -0.140762 | -0.176980 | 0.377058 | -0.279278 | 0.040427 | -0.382282 | nan | -0.364986 | -0.242940 | 0.331079 | 0.211237 | nan | nan | nan | 0.515546 | -0.152513 | -0.087602 | nan | nan | 0.283410 | 0.149519 | 0.720099 | -0.004961 | -0.174686 | -0.409094 | nan | nan | 1.000000 | -1.000000 | 1.000000 | 0.589324 | 0.275393 | 1.000000 | 1.000000 | -1.000000 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | -1.000000 | nan | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan |
| Anupam Investment Amount | 0.222332 | -0.158157 | 0.152515 | 0.077847 | 0.120184 | 0.119344 | -0.068333 | nan | 0.064985 | 0.301259 | 0.395109 | -0.052256 | -0.157330 | 0.265481 | 0.071019 | 0.510099 | -0.310447 | 0.396726 | nan | nan | 0.519919 | -0.188450 | 0.515652 | -0.445133 | 0.384863 | -0.473367 | nan | nan | 1.000000 | -0.535728 | nan | 1.000000 | -0.219316 | 1.000000 | 1.000000 | 0.173510 | 0.710196 | 1.000000 | -0.368718 | nan | 1.000000 | -0.026016 | nan | 1.000000 | -0.702232 | nan | 1.000000 | -0.848930 | nan | 1.000000 | 0.023255 | nan | nan | nan | nan | nan | nan | nan | nan | -0.092450 |
| Anupam Investment Equity | -0.376758 | -0.192655 | -0.395067 | -0.013993 | -0.237157 | -0.207503 | 0.057210 | nan | -0.081840 | -0.264331 | -0.179823 | -0.052607 | 0.660418 | 0.033219 | 0.699011 | -0.209983 | 0.532555 | -0.303209 | nan | nan | -0.277993 | 0.795484 | 0.008170 | -0.692034 | -0.357971 | -0.505823 | nan | nan | -0.535728 | 1.000000 | nan | -0.219316 | 1.000000 | -1.000000 | 0.173510 | 1.000000 | 0.018616 | -0.368718 | 1.000000 | nan | -0.026016 | 1.000000 | nan | -0.702232 | 1.000000 | nan | -0.848930 | 1.000000 | nan | 0.023255 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | -0.147575 |
| Anupam Debt Amount | 0.317895 | -0.107538 | 0.233881 | 0.574076 | -0.280335 | 0.113837 | nan | nan | -0.439941 | 0.245613 | 0.318739 | 0.533769 | nan | nan | nan | 0.719931 | -0.166422 | 0.502577 | nan | nan | 0.569578 | -0.076059 | 0.802924 | 0.392911 | -0.233960 | -0.262826 | nan | nan | nan | nan | nan | 1.000000 | -1.000000 | 1.000000 | 0.710196 | 0.018616 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | -1.000000 |
| Aman Investment Amount | 0.083637 | -0.034936 | 0.062384 | -0.147026 | 0.076863 | 0.183658 | 0.106950 | nan | 0.040536 | 0.089555 | 0.565563 | -0.279248 | 0.033958 | 0.568731 | -0.257537 | 0.464068 | -0.365176 | 0.313034 | nan | nan | 0.510879 | -0.142621 | 0.255011 | 0.512840 | 0.357411 | -0.435428 | nan | nan | 1.000000 | -0.341252 | 1.000000 | 1.000000 | -0.373616 | nan | 1.000000 | -0.368718 | nan | 1.000000 | 0.205214 | 0.305398 | 0.941017 | -0.359914 | 1.000000 | 1.000000 | -0.285299 | nan | 1.000000 | -0.221582 | nan | 0.796005 | -0.035164 | -0.638647 | nan | nan | nan | nan | nan | nan | nan | 0.218031 |
| Aman Investment Equity | -0.290386 | -0.141009 | -0.299713 | -0.032074 | -0.002672 | 0.025831 | 0.338261 | nan | -0.081253 | -0.129570 | -0.309680 | 0.026779 | 0.438284 | -0.675184 | -0.085186 | -0.238795 | 0.334861 | -0.279927 | nan | nan | -0.157542 | 0.761101 | -0.389433 | -0.881064 | -0.335226 | -0.272468 | nan | nan | -0.341252 | 1.000000 | -1.000000 | -0.373616 | 1.000000 | nan | -0.368718 | 1.000000 | nan | 0.205214 | 1.000000 | 0.106403 | -0.396135 | 0.998493 | -1.000000 | -0.285299 | 1.000000 | nan | -0.221582 | 1.000000 | nan | 0.145482 | 0.871526 | 0.988411 | nan | nan | nan | nan | nan | nan | nan | 0.159303 |
| Aman Debt Amount | -0.027655 | -0.438680 | -0.193498 | 0.014568 | 0.339361 | -0.059643 | 0.195316 | nan | 0.021341 | -0.512487 | -0.468403 | -0.864464 | 0.973684 | 1.000000 | nan | 0.199305 | 0.135962 | -0.211592 | nan | nan | -0.032224 | 0.085786 | 0.422350 | -0.207624 | -0.111052 | -0.371674 | nan | nan | 1.000000 | -1.000000 | 1.000000 | nan | nan | nan | nan | nan | nan | 0.305398 | 0.106403 | 1.000000 | 1.000000 | -1.000000 | 1.000000 | nan | nan | nan | nan | nan | nan | 0.998645 | 0.822778 | 0.899521 | nan | nan | nan | nan | nan | nan | nan | -0.068380 |
| Peyush Investment Amount | 0.189545 | -0.066868 | 0.167375 | -0.284248 | 0.024934 | 0.024584 | -0.189834 | nan | 0.066791 | 0.015215 | 0.327598 | -0.229211 | -0.310531 | nan | nan | 0.587167 | -0.198120 | 0.343128 | nan | nan | 0.615546 | -0.174186 | 0.544555 | 0.050443 | 0.643808 | -0.366745 | nan | -1.000000 | 0.906842 | -0.473162 | 1.000000 | 0.833330 | -0.359624 | nan | 1.000000 | -0.026016 | nan | 0.941017 | -0.396135 | 1.000000 | 1.000000 | -0.033575 | 0.402295 | 1.000000 | -0.450754 | nan | 1.000000 | 0.599762 | nan | 0.859351 | -0.094667 | nan | nan | nan | nan | nan | nan | nan | nan | -0.000434 |
| Peyush Investment Equity | -0.273924 | 0.016492 | -0.256414 | -0.117426 | -0.275373 | -0.192138 | 0.009135 | nan | -0.023587 | -0.200131 | -0.144140 | 0.077915 | 0.326559 | nan | nan | -0.210585 | 0.560722 | -0.245236 | nan | nan | -0.292754 | 0.911218 | -0.448814 | -0.766907 | -0.253990 | -0.382562 | nan | 1.000000 | -0.422597 | 0.997131 | -0.628619 | -0.292998 | 0.998008 | nan | -0.026016 | 1.000000 | nan | -0.359914 | 0.998493 | -1.000000 | -0.033575 | 1.000000 | -0.401912 | -0.450754 | 1.000000 | nan | 0.599762 | 1.000000 | nan | -0.220911 | 0.991519 | nan | nan | nan | nan | nan | nan | nan | nan | -0.242395 |
| Peyush Debt Amount | 0.480248 | 0.185670 | 0.460870 | 0.071980 | 0.501527 | 0.382420 | -0.628539 | nan | -0.131753 | 0.000000 | 0.949158 | -0.057992 | nan | nan | nan | 0.518195 | -0.637381 | 0.270917 | nan | nan | 0.374517 | -0.465025 | 0.643727 | -0.453990 | 0.459398 | 0.032769 | nan | nan | 1.000000 | -0.628619 | 1.000000 | nan | nan | nan | nan | nan | nan | 1.000000 | -1.000000 | 1.000000 | 0.402295 | -0.401912 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Amit Investment Amount | -0.096492 | -0.172148 | -0.179683 | -0.421349 | -0.199916 | -0.111801 | -0.157921 | nan | 0.065055 | 0.224041 | 0.247172 | -0.604607 | -0.529238 | -0.086588 | nan | 0.531077 | -0.544839 | 0.342471 | nan | nan | 0.497227 | -0.360431 | -0.164013 | -0.114708 | 0.403495 | -0.394250 | nan | nan | 1.000000 | -0.570674 | nan | 1.000000 | -0.641754 | nan | 1.000000 | -0.702232 | nan | 1.000000 | -0.285299 | nan | 1.000000 | -0.450754 | nan | 1.000000 | -0.014002 | -0.066443 | nan | nan | nan | 1.000000 | -0.962400 | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Amit Investment Equity | -0.194977 | -0.258796 | -0.330110 | 0.035042 | -0.108593 | -0.297954 | 0.663184 | nan | -0.134876 | -0.329391 | -0.271093 | 0.099229 | 0.358334 | 0.363713 | nan | -0.425723 | 0.301131 | -0.308730 | nan | nan | -0.336586 | 0.748191 | -0.823649 | -0.866025 | -0.331164 | -0.358344 | nan | nan | -0.570674 | 1.000000 | nan | -0.641754 | 1.000000 | nan | -0.702232 | 1.000000 | nan | -0.285299 | 1.000000 | nan | -0.450754 | 1.000000 | nan | -0.014002 | 1.000000 | -0.892073 | nan | nan | nan | -0.962400 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Amit Debt Amount | 0.103882 | 0.946028 | 0.510863 | -0.544331 | 0.722705 | 0.760469 | -0.711328 | nan | -0.328502 | 0.662975 | 0.587076 | -1.000000 | -1.000000 | nan | nan | 0.763852 | -0.781745 | 0.591304 | nan | nan | -0.329343 | -0.829397 | 0.984213 | 0.976221 | 0.481815 | -0.722705 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | -0.066443 | -0.892073 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ashneer Investment Amount | nan | -0.266455 | -0.261785 | 0.145770 | -0.078089 | -0.169740 | nan | nan | 0.311533 | 0.649849 | 0.151069 | -0.670061 | nan | nan | nan | 0.385142 | -0.350304 | 0.356795 | nan | nan | 0.610433 | -0.234626 | -1.000000 | nan | 0.695487 | -0.430449 | nan | nan | 1.000000 | 0.178222 | nan | 1.000000 | -0.047601 | nan | 1.000000 | -0.848930 | nan | 1.000000 | -0.221582 | nan | 1.000000 | 0.599762 | nan | nan | nan | nan | 1.000000 | -0.039823 | -1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ashneer Investment Equity | nan | -0.182477 | -0.163796 | -0.332778 | -0.278986 | -0.169891 | nan | nan | -0.147427 | -0.291415 | -0.460915 | 0.707695 | nan | nan | nan | -0.179681 | 0.766988 | -0.344763 | nan | nan | -0.117275 | 0.885537 | -1.000000 | nan | -0.422535 | -0.096582 | nan | nan | 0.178222 | 1.000000 | nan | -0.047601 | 1.000000 | nan | -0.848930 | 1.000000 | nan | -0.221582 | 1.000000 | nan | 0.599762 | 1.000000 | nan | nan | nan | nan | -0.039823 | 1.000000 | -1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ashneer Debt Amount | nan | 1.000000 | 1.000000 | 1.000000 | nan | 1.000000 | nan | nan | -1.000000 | nan | nan | nan | nan | nan | nan | 1.000000 | -1.000000 | 1.000000 | nan | nan | -1.000000 | -1.000000 | 1.000000 | nan | -1.000000 | -1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | -1.000000 | -1.000000 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Guest Investment Amount | 0.270924 | -0.347703 | 0.173347 | 0.235303 | -0.036984 | -0.179377 | -0.099306 | nan | -0.019907 | 0.156418 | 0.608080 | -0.355616 | -0.516589 | 1.000000 | nan | 0.360845 | -0.237020 | 0.187570 | nan | nan | 0.661743 | -0.079096 | 0.772072 | -0.217775 | 0.268676 | -0.296979 | nan | nan | 1.000000 | -0.271580 | nan | 0.862329 | -0.360294 | -1.000000 | 1.000000 | 0.023255 | nan | 0.796005 | 0.145482 | 0.998645 | 0.859351 | -0.220911 | nan | 1.000000 | -0.962400 | nan | nan | nan | nan | 1.000000 | 0.309836 | 0.809262 | nan | nan | nan | nan | nan | nan | nan | 0.233351 |
| Guest Investment Equity | -0.402261 | -0.070913 | -0.449065 | 0.150895 | -0.230962 | -0.241198 | 0.105189 | nan | -0.203369 | -0.372766 | -0.335827 | -0.346967 | 0.767058 | 1.000000 | nan | -0.168632 | 0.546484 | -0.345155 | nan | nan | -0.022326 | 0.849757 | 0.341584 | 0.087682 | -0.379049 | -0.327519 | nan | nan | -0.271580 | 1.000000 | nan | -0.273027 | 0.992607 | nan | 0.023255 | 1.000000 | nan | -0.035164 | 0.871526 | 0.822778 | -0.094667 | 0.991519 | nan | -0.962400 | 1.000000 | nan | nan | nan | nan | 0.309836 | 1.000000 | 0.503827 | nan | nan | nan | nan | nan | nan | nan | -0.143504 |
| Guest Debt Amount | 0.268711 | 0.167497 | 0.565519 | 0.303579 | -0.952982 | -0.372594 | nan | nan | -0.359027 | 0.298133 | 0.917171 | 0.508576 | nan | nan | nan | 0.891364 | 0.187940 | 0.103562 | nan | nan | 0.620285 | 0.322929 | 0.964181 | -0.081987 | -0.010826 | 0.706672 | nan | nan | nan | nan | nan | -1.000000 | nan | 1.000000 | nan | nan | nan | -0.638647 | 0.988411 | 0.899521 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.809262 | 0.503827 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | 0.641304 |
| Namita Present | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Vineeta Present | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Anupam Present | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Aman Present | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Peyush Present | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Amit Present | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ashneer Present | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Guest Present | 0.257709 | -0.126931 | 0.228354 | 0.069559 | -0.038172 | -0.089400 | 0.047938 | nan | 0.021327 | 0.051283 | 0.008203 | 0.257626 | 0.175889 | 0.666776 | -0.109575 | -0.034174 | -0.189596 | 0.037627 | 0.104706 | 0.067850 | 0.135177 | -0.189206 | 0.213265 | 0.294880 | 0.004660 | 0.021358 | nan | 0.880812 | nan | nan | nan | 0.209982 | -0.303400 | nan | -0.092450 | -0.147575 | -1.000000 | 0.218031 | 0.159303 | -0.068380 | -0.000434 | -0.242395 | nan | nan | nan | nan | nan | nan | nan | 0.233351 | -0.143504 | 0.641304 | nan | nan | nan | nan | nan | nan | nan | 1.000000 |
print("numpy version: {}". format(np.__version__))
print("pandas version: {}". format(pd.__version__))
import matplotlib
print("matplotlib version: {}". format(matplotlib. __version__))
print("seaborn version: {}". format(sns.__version__))
import plotly
print("plotly version: {}". format(plotly.__version__))
# Current Python package versions
# numpy version: 1.26.4
# pandas version: 2.2.0
# matplotlib version: 3.7.5
# seaborn version: 0.12.2
# plotly version: 5.18.0
numpy version: 1.26.4 pandas version: 2.2.0 matplotlib version: 3.7.5 seaborn version: 0.12.2 plotly version: 5.18.0
shark_tank.loc[shark_tank['Number of Presenters'] != shark_tank['Male Presenters'].fillna(0) + shark_tank['Female Presenters'] + shark_tank['Transgender Presenters'].fillna(0)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Male Presenters'].isnull()) & (shark_tank['Couple Presenters'] == 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Female Presenters'].isnull()) & (shark_tank['Couple Presenters'] == 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Accepted Offer'] == 1) & (shark_tank['Total Deal Amount'].isnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Accepted Offer'] == 1) & (shark_tank['Number of Sharks in Deal'].isnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Accepted Offer'].isnull()) & (shark_tank['Number of Sharks in Deal'] >= 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[round(shark_tank['Total Deal Amount'].fillna(0),1) != round(shark_tank['Ashneer Investment Amount'].fillna(0) + shark_tank['Namita Investment Amount'].fillna(0) + shark_tank['Anupam Investment Amount'].fillna(0) + shark_tank['Vineeta Investment Amount'].fillna(0) + shark_tank['Aman Investment Amount'].fillna(0) + shark_tank['Peyush Investment Amount'].fillna(0) + shark_tank['Amit Investment Amount'].fillna(0) + shark_tank['Guest Investment Amount'].fillna(0), 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[round(shark_tank['Total Deal Equity'].fillna(0),1) != round(shark_tank['Ashneer Investment Equity'].fillna(0) + shark_tank['Namita Investment Equity'].fillna(0) + shark_tank['Anupam Investment Equity'].fillna(0) + shark_tank['Vineeta Investment Equity'].fillna(0) + shark_tank['Aman Investment Equity'].fillna(0) + shark_tank['Peyush Investment Equity'].fillna(0) + shark_tank['Amit Investment Equity'].fillna(0) + shark_tank['Guest Investment Equity'].fillna(0),1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[round(shark_tank['Total Deal Debt'].fillna(0),1) != round(shark_tank['Ashneer Debt Amount'].fillna(0) + shark_tank['Namita Debt Amount'].fillna(0) + shark_tank['Anupam Debt Amount'].fillna(0) + shark_tank['Vineeta Debt Amount'].fillna(0) + shark_tank['Aman Debt Amount'].fillna(0) + shark_tank['Peyush Debt Amount'].fillna(0) + shark_tank['Amit Debt Amount'].fillna(0) + shark_tank['Guest Debt Amount'].fillna(0),1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Received Offer'] == 1) & (shark_tank['Accepted Offer'].isnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Received Offer'] == 0) & (shark_tank['Accepted Offer'].notnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Number of Sharks in Deal'].fillna(0).round(0).astype(int) != shark_tank['Ashneer Investment Amount'].notnull().astype("int") + shark_tank['Namita Investment Amount'].notnull().astype("int") + shark_tank['Anupam Investment Amount'].notnull().astype("int") + shark_tank['Vineeta Investment Amount'].notnull().astype("int") + shark_tank['Aman Investment Amount'].notnull().astype("int") + shark_tank['Peyush Investment Amount'].notnull().astype("int") + shark_tank['Amit Investment Amount'].notnull().astype("int") + shark_tank['Guest Investment Amount'].notnull().astype("int")) & (shark_tank['Guest Present']<2) ]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Couple Presenters'] != 0) & (shark_tank['Couple Presenters'] != 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Received Offer'] != 0) & (shark_tank['Received Offer'] != 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Accepted Offer'] != 0) & (shark_tank['Accepted Offer'] != 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Ashneer Investment Amount'].notnull()) & (shark_tank['Ashneer Present'] != 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Namita Investment Amount'].notnull()) & (shark_tank['Namita Present'] != 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Anupam Investment Amount'].notnull()) & (shark_tank['Anupam Present'] != 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Vineeta Investment Amount'].notnull()) & (shark_tank['Vineeta Present'] != 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Aman Investment Amount'].notnull()) & (shark_tank['Aman Present'] != 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Peyush Investment Amount'].notnull()) & (shark_tank['Peyush Present'] != 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Amit Investment Amount'].notnull()) & (shark_tank['Amit Present'] != 1)]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Guest Investment Amount'].notnull()) & (shark_tank['Guest Present'].isnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Guest Investment Amount'].notnull()) & (shark_tank['Invested Guest Name'].isnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['All Guest Names'].isnull()) & (shark_tank['Guest Present'].notnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Total Deal Debt'].isnull()) & (shark_tank['Debt Interest'].notnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Received Offer'] == 0) & (shark_tank['Deal Has Conditions'].notnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Accepted Offer'] == 0) & (shark_tank['Royalty Deal'].notnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns
shark_tank.loc[(shark_tank['Accepted Offer'] == 0) & (shark_tank['Advisory Shares Equity'].notnull())]
| Season Number | Startup Name | Episode Number | Pitch Number | Season Start | Season End | Original Air Date | Episode Title | Anchor | Industry | Business Description | Company Website | Started in | Number of Presenters | Male Presenters | Female Presenters | Transgender Presenters | Couple Presenters | Pitchers Average Age | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Net Margin | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Amit Investment Amount | Amit Investment Equity | Amit Debt Amount | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Guest Investment Amount | Guest Investment Equity | Guest Debt Amount | Invested Guest Name | All Guest Names | Namita Present | Vineeta Present | Anupam Present | Aman Present | Peyush Present | Amit Present | Ashneer Present | Guest Present |
|---|
0 rows × 78 columns